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Research Article
This study establishes a reproducible protocol for identifying and quantifying Traditional Chinese Medicine (TCM) syndrome elements in patients with gastric cancer, enabling systematic analysis through cluster, correlation, and principal component analysis methods.
Gastric cancer remains a major cause of morbidity and mortality worldwide, with a particularly high burden in China. Traditional Chinese Medicine (TCM) emphasizes holistic regulation and syndrome differentiation, yet reproducible methods for quantifying syndrome elements in gastric cancer are limited. We conducted a cross-sectional study of 60 patients at Wuxi Second Traditional Chinese Medicine Hospital (December 2022-March 2024) and present a standardized, reproducible protocol for syndrome-element evaluation. Demographics, medical history, and TCM diagnostic information were captured with a standardized case-report form; syndrome elements were identified using a weighted scoring system with dual-expert adjudication. Analysis followed a prespecified multivariate pipeline-two-stage H-K clustering (hierarchical Ward.D2 to suggest k followed by k-means refinement), correlation-network mapping, and principal component analysis. The most frequent elements were qi stagnation, qi deficiency, blood stasis, spleen deficiency, and dampness; strong associations were observed between qi stagnation and blood stasis and between qi deficiency and blood stasis, with additional links involving dampness/phlegm. Principal component analysis indicated that a small set of elements accounted for the largest share of between-patient variation, supporting clinically meaningful stratification. This protocol operationalizes TCM syndrome-element assessment into measurable steps with shareable forms and code, enhancing objectivity and reproducibility and providing a methodological basis for clinical decision-making and multicenter validation. The framework is adaptable to other malignancies, facilitating broader integration of TCM within evidence-based oncology.
Gastric cancer remains one of the most prevalent and lethal malignancies worldwide, with China bearing a disproportionately high incidence and mortality burden1,2. Despite advances in surgical resection, chemotherapy, and targeted therapies, the overall prognosis remains unsatisfactory, underscoring the need for complementary approaches that address both disease biology and patient quality of life3. Traditional Chinese Medicine (TCM), emphasizing holistic regulation and syndrome differentiation, is widely used in oncology; however, the lack of standardized, replicable methods for syndrome identification has limited systematic integration of TCM into evidence-based cancer care4,5.
Recent developments in syndrome element differentiation provide a structured framework to quantify the key pathological factors underlying TCM syndromes, enabling more precise patient classification and tailored interventions6. Applying this quantitative framework to gastric cancer offers a way to bridge traditional theory with modern analytics, yet reproducible, protocolized workflows remain scarce7. To our knowledge, this is the first protocol to operationalize syndrome-element assessment in gastric cancer with explicit forms, dual-expert adjudication, and a prespecified multivariate pipeline (two-stage H-K clustering, correlation network, and PCA), packaged for replication and multicenter adoption. By translating syndrome differentiation into measurable variables and prespecifying analytic parameters, the present study provides a replicable foundation for clinical practice and multicenter research, clarifies the distribution of key elements in gastric cancer populations, and offers a concrete path toward more precise TCM-based oncology strategies.
Ethics and Oversight
This study was approved by the Ethics Committee of Wuxi Second Traditional Chinese Medicine Hospital (20220904). All participants provided written informed consent in accordance with the Declaration of Helsinki; procedures were consistent with ICH-GCP principles. Any identifiable media used for demonstration was separately consented. To reduce assessment bias, physicians who evaluated TCM syndrome elements were blinded to the American Joint Committee on Cancer (AJCC) stage and treatment status throughout scoring and adjudication; blinding was maintained by a data steward who removed those fields from assessor-facing sheets.
Patient recruitment and case sources
Consecutive patients with gastric cancer were enrolled between December 1, 2022, and March 31, 2024, from the outpatient and inpatient departments of Wuxi Second TCM Hospital. Screening was logged prospectively (order approached, eligibility decision, reason for exclusion, consent outcome). A de-identified screening checklist and the blank case-report form (CRF) are provided as a table in the public repository under Data Availability. Baseline demographics and staging summaries for the enrolled cohort are presented in Table 1 to contextualize subsequent analyses.
Diagnostic and staging criteria
Diagnosis followed the norms for the diagnosis and treatment of common malignant tumors issued by the Medical Administration Department of the Ministry of Health of the PRC, with pathological confirmation required in every case. Tumor stage was assigned according to the AJCC Cancer Staging Manual, 8th edition, and verified against pathology and imaging source documents before enrollment8.
Eligibility criteria and withdrawal rules
Adults (≥18 years) with pathologically confirmed gastric cancer and Karnofsky Performance Score (KPS) ≥ 60 were eligible, provided they could complete the standardized TCM assessment. Exclusions included pregnancy, unstable severe comorbidities compromising consent/safety, or incomplete core baseline data. Patients receiving or having received chemotherapy, radiotherapy, or targeted/immunotherapy were eligible; treatment status was recorded (Never/Ongoing/Past) and used in stratified and sensitivity analyses. Participants could be withdrawn if data were insufficient for syndrome differentiation, if rapid clinical deterioration occurred, or upon personal request, without impact on subsequent care.
Data collection and management
Trained researchers completed a standardized CRF titled Collection Form of Syndrome Element Information for Gastric Cancer Patients. Data were double-entered using spreadsheet software (version ≥15.30), independently cross-checked, and reconciled before database lock. A data dictionary, coding sheet, and audit log were maintained from the first entry to analysis. Analytical datasets were de-identified and stored with role-based access control; a frozen, time-stamped copy was archived to support replication and auditing.
Standardized TCM assessment
Clinical evaluation adhered to the four classical examinations-inspection, listening/smelling, inquiry, palpation-in a fixed sequence under consistent lighting (~5,000-6,500 K; CRI ≥90). Patients were seated facing the light source; tongue and pulse features were recorded using predefined descriptors. Assessors underwent calibration with pilot cases and exemplar vignettes before formal scoring. All observations were recorded immediately on the CRF to minimize recall error. The cohort-level distribution of composite TCM syndrome patterns derived from this assessment is summarized in Table 2 (footnotes specify whether a single primary pattern or multiple patterns were allowed).
Syndrome-element scoring and adjudication
Items were scored according to the Syndrome Element Scale (full item list and codes in Supplementary Table 1). Inter-rater reliability (present/absent) was quantified using Cohen's κ with 95% CIs and summarized in Supplementary Table 2. Severity weights were: mild = 0.7, moderate = 1.0, severe = 1.5. Weighted items were summed within each element to yield the element score. Presence of an element was defined at threshold = 14; robustness at 12 and 16 is reported in Supplementary Table 3. Two TCM oncology physicians independently scored each case; discordances were resolved in a consensus meeting with a written rationale. Element-level prevalence across patients is reported in Table 3, and a worked example of item-to-element scoring is shown in Supplementary Figure 1.
Statistical analysis
Software and reproducibility
All analyses were performed in R (v4.3). A runnable, version-locked script and the full sessionInfo() output are archived with the dataset (https://doi.org/10.6084/m9.figshare.30405196.v2). A fixed random seed was set before all stochastic procedures. Core functions/packages were: base stats (prcomp, hclust, kmeans), cluster (silhouette), and igraph (network visualization)9. The element-level distribution is summarized in Supplementary Table 1, and the blank case-report form (CRF), data dictionary, and tidy input matrices are available in the public repository referenced in the Data Availability section.
Two-stage H - K clustering
A two-stage procedure was used to derive clinically interpretable groups.
Hierarchical stage: Patient-by-element weighted scores were z-standardized, Euclidean distances were computed, and agglomeration used Ward.D2 linkage. The dendrogram was inspected to propose candidate k using joint consideration of the elbow in within-cluster dispersion, branch stability, and preliminary silhouette patterns10.
k-means refinement. For each candidate k, k-means was initialized from the hierarchical partitions and run with nstart ≥ 50; final membership minimized within-cluster sum of squares. Average silhouette width and per-cluster profiles were exported for traceability. The integral heat map summarizing standardized scores is shown in Figure 1; the corresponding dendrogram is shown in Figure 2.
Principal component analysis and correlation network
Element-score matrices were centered and scaled, and PCA was performed with prcomp(center = TRUE, scale = TRUE). The node size reflects the prevalence of elements; labels were manually checked to ensure no overlap and readability. The resulting correlation network is shown in Figure 3. The PCA biplot is shown in Figure 4. Pairwise Pearson correlations across elements were computed with pairwise-complete observations; edges were retained when |r| ≥ 0.30, with edge width proportional to |r|. A force-directed algorithm (Fruchterman-Reingold) was used for layout to improve readability11.
Missing data and sensitivity
Per-variable completeness (counts, %) is summarized in Supplementary Table 4; because missingness was low, complete-case analysis was used for all primary outputs. Robustness was assessed by recomputing element-presence classification under alternative score cutoffs (12 and 16) relative to the prespecified threshold of 14 (Supplementary Table 3), and-where relevant-within treatment-status strata (Never/Ongoing/Past; Supplementary Table 5). Inter-rater reliability for element adjudication by two independent TCM experts was high (overall agreement 95%), as detailed in Supplementary Table 2. A worked example illustrating item-to-element scoring and thresholding is provided in Supplementary Figure 1.
Quality control, timing, and troubleshooting
Quality assurance included weekly 10% source-data verification, periodic assessor recalibration, and automated range/logic checks before database lock. Typical timelines were ~10-15 min for screening/consent, 20-30 min for standardized TCM assessment and scoring, and 10-15 min for data entry/verification per case. The critical steps here include maintaining stable lighting and a fixed examination sequence; preserving assessor blinding to stage and treatment; and documenting reasons for any adjudication. When summarizing distributions, denominators were explicitly stated as N (patients) or non-exclusive element counts, and the same denominator was used consistently across text, tables, and figure captions.
Participant characteristics
Sixty patients with gastric cancer were enrolled between December 2022 and March 2024. Age distribution was: 41-50 years, 9/60 (15.0%); 51-60 years, 18/60 (30.0%); 61-70 years, 15/60 (25.0%); 71-80 years, 13/60 (21.7%); and 81-90 years, 5/60 (8.3%). Overall sex distribution was male 37/60 (61.7%) and female 23/60 (38.3%). Counts by sex within each age band are reported in Table 1, which provides a baseline context for subsequent analyses.
Distribution of TCM syndrome patterns
Among composite TCM patterns, the most frequent was qi stagnation with blood stasis (11/60, 18.3%), followed by phlegm-blood stasis interlocking (7/60, 11.7%) and qi deficiency with blood stasis (6/60, 10.0%). All remaining patterns each occurred in ≤8.3% of patients (n = 5, 3.3%, or 1.7% per pattern) and are listed with exact counts in Table 2. Overall, patterns involving qi stagnation and blood stasis predominated in this cohort.
Distribution of syndrome elements
At the element level, qi stagnation was present in 46/60 (76.7%), qi deficiency in 40/60 (66.7%), blood stasis in 37/60 (61.7%), spleen deficiency in 29/60 (48.3%), dampness in 17/60 (28.3%), and phlegm in 16/60 (26.7%). Because elements are non-mutually exclusive, percentages use patients (N = 60) as the denominator and do not sum to 100%. The full distribution is provided in Table 3.
Heat map and cluster structure
An integral heat map of the patient-by-element weighted score matrix (Figure 1) showed high aggregate intensities for qi stagnation, qi deficiency, blood stasis, and dampness across much of the cohort. Hierarchical clustering using Euclidean distance and Ward.D2 linkage (Figure 2) grouped elements into stable clusters dominated by these high-prevalence features, with close relationships between qi stagnation and blood stasis, between qi deficiency and blood stasis, and between dampness and phlegm.
Correlation network
The network visualization of pairwise Pearson correlations (Figure 3) highlighted strong co-occurrence of qi stagnation with blood stasis and of dampness with phlegm, with additional links (e.g., involving yang deficiency) consistent with the cluster structure.
Principal component analysis
Principal component analysis of the z-standardized element-score matrix (Figure 4) separated patients primarily along two axes. PC1 explained 25.0% and PC2 explained 20.8% of the variance. PC1 was driven chiefly by qi stagnation, qi deficiency, and blood stasis, whereas PC2 reflected dampness and phlegm. Together, these components delineated clinically interpretable subgroups.
Inter-rater reliability
Agreement between the two TCM experts on syndrome-element assignment was evaluated using Cohen's K with 95% confidence intervals. Detailed estimates (overall and per element) are provided in Supplementary Table 2 and confirm reproducible element identification within the protocol.
Threshold sensitivity analyses
Element presence was defined by a weighted-score cutoff of 14. Sensitivity analyses at cutoffs 12 and 16 were performed; summary results are reported in Supplementary Table 3 and demonstrate that the principal findings (high-prevalence elements and key co-occurrences) were not materially altered by reasonable changes to the cutoff.
Data completeness
Per-variable completeness and missingness are summarized in Supplementary Table 4. The primary analyses used complete cases; results of a prespecified robustness check (repeat analyses after simple imputation, where applicable) are shown in Supplementary Table 5 and were directionally consistent with the main findings.
Representative worked example
A one-patient worked example illustrates the decision path from raw symptom scores → severity weights → element totals → threshold-based presence/absence. The full sheet and calculations are provided in Supplementary Figure 1 to facilitate replication.
DATA AVAILABILITY:
The dataset used in this study has been publicly archived and is available for use by other researchers. The data can be accessed through Figshare at the following DOI link:https://doi.org/10.6084/m9.figshare.30405196.v1.
Figure 1: Heat map of syndrome-element weighted scores. Patient-by-element matrix of z-standardized weighted scores; darker colors denote higher relative intensity. Rows and columns follow the clustering used in the analysis; a scale bar is included. Please click here to view a larger version of this figure.
Figure 2: Hierarchical cluster tree of syndrome elements. Dendrogram derived from Euclidean distance and Ward. D2 linkage groups elements into major clusters (e.g., qi stagnation, qi deficiency, blood stasis, dampness, yang deficiency) and guides the selection of the number of clusters, which is refined later by k-means. Please click here to view a larger version of this figure.
Figure 3: Correlation network of syndrome elements. Undirected network of Pearson correlations among elements, with edges drawn for |r| > 0.30 and edge width proportional to |r|. Node labels use the standardized English terminology adopted in the protocol. Please click here to view a larger version of this figure.
Figure 4: Principal component analysis (PCA) of syndrome elements.Scatterplot of patients on the first two principal components computed from the z-standardized element-score matrix. PC1 = 25.0% and PC2 = 20.8% variance explained. PC1 reflects qi stagnation, qi deficiency, and blood stasis; PC2 reflects dampness and phlegm. Please click here to view a larger version of this figure.
| Age groups (years) | Female/Male | Frequency | Frequency rate (%) |
| 41–50 | 5,4 | 9 | 15 |
| 51–60 | 7,11 | 18 | 30 |
| 61–70 | 5,10 | 15 | 25 |
| 71–80 | 3,10 | 13 | 21.67 |
| 81–90 | 1,4 | 5 | 8.33 |
Table 1: Age and sex distribution of patients with gastric cancer. Counts and percentages by age band: 41-50, 9/60 (15.0%); 51-60, 18/60 (30.0%); 61-70, 15/60 (25.0%); 71-80, 13/60 (21.7%); 81-90, 5/60 (8.3%). Overall sex distribution: male 37/60 (61.7%), female 23/60 (38.3%). Denominator = N = 60 patients.
| Types | Frequency | Frequency rate (%) |
| Qi stagnation and blood stasis | 11 | 18.3 |
| Phlegm and blood stasis intermingled | 7 | 11.7 |
| Qi deficiency and blood stasis | 6 | 10 |
| Deficiency of qi and blood | 5 | 8.3 |
| Deficiency of kidney-yang | 5 | 8.3 |
| The stasis toxin is inside the knot | 5 | 8.3 |
| Yang deficiency and cold condensation | 4 | 6.7 |
| Deficiency of liver and kidney Yin | 2 | 3.3 |
| Liver depression and spleen deficiency | 2 | 3.3 |
| Qi stagnation of the liver | 2 | 3.3 |
| Spleen deficiency and qi stagnation | 2 | 3.3 |
| Phlegm stagnates | 2 | 3.3 |
| Phlegm dampness is abundant inside | 2 | 3.3 |
| Cold congealing liver pulse | 1 | 1.7 |
| Kidney-yin deficiency | 1 | 1.7 |
| Deficiency of Yang | 1 | 1.7 |
| Yin and Yang deficiency | 1 | 1.7 |
| Blood stasis and poison interlock | 1 | 1.7 |
Table 2: Distribution of TCM syndrome patterns in gastric cancer patients. Frequency and percentage for each pattern are reported (denominator = 60 patients). Most common patterns were qi stagnation with blood stasis, 11/60 (18.3%); phlegm-blood stasis interlocking, 7/60 (11.7%); and qi deficiency with blood stasis, 6/60 (10.0%). Remaining patterns appear at 8.3% (n=5), 3.3% (n=2), or 1.7% (n=1) per pattern as listed in the table.
| Disease location | Frequency | Frequency rate (%) |
| Spleen | 29 | 28.2 |
| Stomach | 29 | 28.2 |
| Liver | 21 | 20.4 |
| Large intestine | 9 | 8.7 |
| Renal | 8 | 7.8 |
| Chest and diaphragm | 6 | 5.8 |
| Heart | 1 | 1 |
| Stagnation of vital energy | 46 | 19.4 |
| Deficiency of vital energy | 40 | 16.9 |
| Blood stasis | 37 | 15.6 |
| Deficiency of Yang | 37 | 15.6 |
| Wet | 17 | 7.2 |
| Deficiency of blood | 16 | 6.8 |
| Phlegm | 12 | 5.1 |
| Deficiency of Yin | 10 | 4.2 |
| Poison | 6 | 2.5 |
| Unconsolidation | 5 | 2.1 |
| Cold | 5 | 2.1 |
| Dyspepsia | 3 | 1.3 |
| Superficial | 2 | 0.8 |
| Yang buoyancy | 1 | 0.4 |
Table 3: Distribution of syndrome elements in gastric cancer patients. Elements are non-mutually exclusive; percentages use patients (N = 60) as the denominator and totals may exceed 100%. Prevalence: qi stagnation, 46/60 (76.7%); qi deficiency, 40/60 (66.7%); blood stasis, 37/60 (61.7%); spleen deficiency, 29/60 (48.3%); dampness, 17/60 (28.3%); phlegm, 16/60 (26.7%).
Supplementary Figure 1: Decision path of syndrome element evaluation. This figure illustrates the decision path from raw symptom scores → severity weights → element totals → threshold-based presence/absence determination. It includes the full sheet and calculations, with the values and calculations at each step clearly shown to ensure the reproducibility of the method. Please click here to download this figure.
Supplementary Table 1: Element-level distribution. This table presents the distribution of each syndrome element across the gastric cancer patient cohort, with the frequency of each element recorded by patient count. It indicates the prevalence and presence of different elements in the sample, providing insight into the most common syndrome elements observed in the patient population. Please click here to download this table.
Supplementary Table 2: Inter-rater reliability for element adjudication. Cohen's kappa (κ) with 95% confidence intervals for six elements across n = 60 cases; overall percent agreement is 95.0%. Presence was adjudicated using the prespecified weighted-score rule; higher κ indicates stronger agreement. Please click here to download this table.
Supplementary Table 3: Sensitivity to cutoff (12, 14, 16). Counts of positive classifications for each element under three alternative thresholds (12, 14, 16). "Direction" denotes the expected monotonic pattern (c12 ≥ c14 ≥ c16). Positive status is defined as a weighted score ≥ the stated cutoff. Please click here to download this table.
Supplementary Table 4: Variable completeness and missingness. Available and missing counts, with percent missing, for demographic/clinical variables and element presence/absence. KPS denotes Karnofsky Performance Status. Please click here to download this table.
Supplementary Table 5: Sensitivity by treatment-status strata. Positive counts for each element at cutoff 14, stratified by treatment status (Never n = 22; Ongoing n = 19; Past n = 19), with totals shown. Used to assess the robustness of element distributions across strata. Please click here to download this table.
This study developed and applied a standardized, protocol-centric workflow to identify and quantify TCM syndrome elements in gastric cancer. By combining a weighted scoring system, dual-expert adjudication, and a prespecified multivariate pipeline-two-stage H-K clustering, correlation network analysis, and principal component analysis-the method consistently highlighted qi stagnation, qi deficiency, blood stasis, and dampness as the most frequent and clinically relevant elements in this cohort, in line with prior observations12,13. Beyond confirming prevalence, the contribution here is methodological: a reproducible, video-ready framework that specifies how data are captured, scored, adjudicated, and analyzed, thereby helping bridge traditional diagnostic practices with contemporary quantitative research in oncology14,15.
Syndrome-element co-occurrence patterns-particularly qi stagnation with blood stasis, qi deficiency with blood stasis, and dampness with phlegm-emerged across both clustering and network analyses16,17. These pairings align with classical pathomechanisms in which constrained qi flow promotes stasis and pain, deficiency predisposes to stasis, and spleen-transport failure fosters dampness that condenses into phlegm18. From an oncological perspective, such structures may correspond to intertwined pathways of impaired motility, microcirculatory stagnation, inflammation, and metabolic dampness-phlegm phenotypes, offering a TCM lens on symptom clusters observed in gastric cancer19. Consistently, PCA showed that a small set of elements captured a substantial proportion of between-patient variance, supporting dimension reduction for stratification and hypothesis generation20,21.
The clinical implications are twofold. First, quantifying elements improves objectivity and reproducibility in syndrome differentiation relative to purely descriptive approaches22,23. Second, the co-occurrence and component structure provide actionable cues for individualized interventions: patients dominated by qi stagnation-blood stasis patterns may prioritize circulation-promoting and stasis-resolving strategies24; those with qi deficiency-blood stasis may require qi-tonifying plus anti-stasis approaches25; and those with dampness/phlegm patterns may benefit from spleen-strengthening and damp-resolving regimens26,27. These insights can be prospectively operationalized as algorithmic decision rules tied to element thresholds and cluster membership, facilitating protocolized TCM care that interfaces with evidence-based oncology pathways28,29.
Several safeguards and limitations merit discussion. To enhance reliability, we prespecified assessor training, blinding to stage/treatment, and dual-expert scoring with consensus; inter-rater reliability was quantified using Cohen's κ, supporting reproducibility of element identification within this framework30. The threshold of 14 for element presence was justified a priori and examined via sensitivity analyses at 12/14/16, which preserved the rank order of high-prevalence elements and key co-occurrences; nevertheless, threshold calibration should be revisited in larger samples and with external validation cohorts31. Our H-K pipeline used Ward.D2 linkage with Euclidean distance to estimate cluster number, refined by k-means with multiple starts, and network edges were displayed at |r|>0.30-choices that balance interpretability and stability but remain analytic degrees of freedom32. Missingness was low and handled by complete-case analysis; future work could incorporate multiple imputation and pre-registered analysis plans to further mitigate analytic flexibility. The single-center, cross-sectional design and modest sample size limit generalizability and preclude causal inference; treatment status, stage, comorbidities, and lifestyle factors are potential confounders not fully powered for stratified analyses33.
Future work should pursue multicenter recruitment with standardized training and external validation of the scoring threshold, IRR, and cluster solutions, alongside prospective outcomes to test whether element combinations predict symptoms, quality of life, or treatment tolerance34. Integrating objective markers-for example, digitized tongue imaging, pulse waveform indices, inflammatory or coagulation biomarkers-could triangulate subjective assessments and refine element definitions35. Open sharing of de-identified data, CRFs, full scales, and analysis code will enable meta-analysis and method harmonization across centers, accelerating consensus on element taxonomy and cutoffs36.
In summary, we provide a structured, reproducible protocol for syndrome-element analysis in gastric cancer that enhances the objectivity of differentiation37, aligns multivariate patterns with TCM pathomechanism reasoning38, and offers pragmatic guidance for individualized care and future validation studies39. While larger multicenter and longitudinal research is needed, the present framework delineates a practical path toward standardized, data-driven TCM oncology and facilitates integration with modern clinical research infrastructures.
The authors used an AI-assisted writing tool (ChatGPT, OpenAI) only for language polishing and reference formatting after the scientific content had been drafted by the authors. No study design, data collection, data analysis, figure generation, or scientific interpretation was performed by the tool. No identifiable or sensitive data were entered into the tool. All outputs were independently reviewed and revised by the authors, who take full responsibility for the integrity and accuracy of the manuscript.
The authors thank the staff of Wuxi Second Traditional Chinese Medicine Hospital for their support in patient recruitment and data collection. This work was supported by the Jiangsu Province Wuxi City Traditional Chinese Medicine Administration Bureau, 2022 Annual Science and Technology Project (Project Number: ZYYB23).
| Case Report Form (CRF) | Wuxi Second TCM Hospital | CRF-001 | Standardized form for collecting patient data |
| cluster R Package | CRAN | R-cluster-008 | R package for clustering and silhouette analysis |
| Examination Light (5,000–6,500 K) | Philips | EX-LIGHT-003 | Light used for TCM examination |
| igraph R Package | CRAN | R-igraph-007 | R package for network visualization |
| Pulse Measuring Device | Omron | PMD-005 | Device for measuring pulse during TCM evaluation |
| R Software | The R Project | R-001 | Open-source software for statistical analysis |
| stats R Package | CRAN | R-Stats-006 | R package for statistical analysis (prcomp, hclust, kmeans) |
| Syndrome Element Scale | Wuxi Second TCM Hospital | SE-Scale-002 | Scoring tool for evaluating syndrome elements |
| Tongue Imaging Device | Canon | TI-IMG-004 | Device used to capture tongue images |
