Clinician-Validated Taxonomy

• Clinician-validated taxonomy is a structured classification system that defines clinical phenomena using expert consensus and gold-standard protocols.
• It integrates categorical, dimensional, and transdiagnostic models to capture diagnostic nuances and enhance computational modeling for practical decision support.
• Robust validation methodologies combine iterative expert review with statistical and psychometric assessment to ensure high reliability and clinical actionability.

A clinician-validated taxonomy is a structured classification or coding system for clinical phenomena, error types, risk states, or tasks, where categories and labels are anchored either in direct expert consensus, gold-standard clinical protocols, or systematically validated construct definitions. Such taxonomies form the substrate for high-fidelity computational modeling, evaluation frameworks, and real-world clinical decision support across a range of domains, including psychopathology, digital health monitoring, diagnostic reasoning, conversational agent safety, and clinical NLP. They are distinguished by rigorous, replicable validation workflows leveraging quantitative agreement metrics, iterative expert review, and alignment to established clinical standards, thereby exceeding the scientific validity, reliability, and interpretability of purely data-driven or self-assigned classifications.

1. Principles and Motivations for Clinician-Validated Taxonomies

The central rationale for clinician validation lies in the hierarchy of measurement validity in clinical science. Gold-standard categories or states are grounded in structured/semi-structured clinician-administered instruments (e.g., SCID-5, ADIS-5L) aligning directly with nosological criteria such as DSM-5 or ICD-10. Secondary tiers employ well-validated questionnaires (e.g., PHQ-9, GAD-7), exhibiting strong reliability ($\alpha > 0.80$) and convergent validity; the lowest tier includes self-declared or proxy measures lacking external validation and prone to misclassification and bias ($\kappa < 0.50$) (Shani et al., 4 Apr 2025).

Consequences of using inadequately validated or unstandardized taxonomies include increased misclassification, attenuated statistical power (especially in the presence of dichotomization), faulty model overfitting, and generalizability loss. Best practice mandates whenever feasible the direct collection of clinician-confirmed data; when not, proxies of known psychometric merit should be used and their limitations detailed.

2. Taxonomic Structures: Categorical, Dimensional, and Transdiagnostic Approaches

Clinical taxonomies traditionally follow three architectures:

• Categorical taxonomy: Discrete class memberships (e.g., "Major Depressive Disorder: yes/no") reflect established diagnostic frameworks (DSM/ICD), offering clinical familiarity but losing sub-threshold nuance and statistical efficiency.
• Dimensional taxonomy: Constructs are continuous (e.g., PHQ-9: $0-27$), supporting more granular statistical inference, improved sub-threshold capture, and reduced Type II error. Latent-trait and taxometric analyses generally support dimensional models over categorical (Shani et al., 4 Apr 2025).
• Transdiagnostic taxonomy: Higher-order latent spectra or factors cut across classical nosology, reflecting empirical comorbidity patterns. Examples include RDoC domains (e.g., Negative Valence, Cognitive Systems), HiTOP spectra, and the p-factor (general psychopathology liability) (Shani et al., 4 Apr 2025). This approach addresses comorbidity, diagnostic crossover, and aligns with shared treatment mechanisms.

Transdiagnostic and dimensional structures are increasingly favored for computational modeling, enabling both cross-condition generalizability and richer phenotype–mechanism mappings.

3. Construction and Validation Methodologies

Clinician-validated taxonomies are developed via one or more of the following workflows:

Qualitative, Quantitative, and Hybrid Approaches

• Expert-driven structuring: Panels of clinicians define categories based on consensus regarding clinical salience and utility (e.g., MedHELM: 29 clinicians, 5 categories, 22 subcategories, 121 tasks; 96.7% mapping agreement) (Bedi et al., 26 May 2025). Iterative workshops and free-text feedback are employed for terminology/range refinement.
• Empirical, data-driven clustering augmented by expert review: Complex feature datasets (e.g., multi-modal pain metrics, actigraphy, speech; (Reinen et al., 2022)) are clustered (e.g., k-means) into candidate state spaces. Clinicians validate cluster interpretability, ensure mapping to known clinical phenomena, and select cut-points or orderings corresponding to real-world gradients of severity or quality-of-life.
• Mixed three-way decision + subjective weighting: The CSA model for psychiatric nosology (three-way partition into acceptance, deferment, rejection) blends direct pairwise clinician ranking ($r(d)$) with quantitative AHP-derived weighting ($w(d)$) and trisection by statistical or quantile thresholds ($\alpha$, $\beta$). Group assignment validation combines agreement metrics (Kendall’s $W$, Cohen’s or Fleiss’s $\kappa$), with clinical feedback loops (Wang et al., 2022).

Statistical and Psychometric Validation

• Reliability metrics: Internal consistency (Cronbach’s $\alpha$), test–retest/stability, and inter-rater reliability (Cohen’s $\kappa$, ICC) with defined target thresholds ($\alpha>0.80$, $\kappa>0.70$, ICC$>0.75$). Periodic recalibration and drift assessments are recommended.
• Latent-structure modeling: EFA and CFA (SEM) with explicit formulas for model fit (CFI, RMSEA, SRMR cutoffs), bifactor models for p-factor estimation, and full-likelihood estimation for parameter fitting (Shani et al., 4 Apr 2025).
• Concordance and performance analysis: For operationalized error or risk taxonomies, clinician–model agreement is measured at multiple hierarchy levels (domain, code), using percent concordance, precision, recall, $F_1$, and McNemar’s test. Example: In RAEC's error taxonomy, retrieval-augmented guardrails achieved 50% concordance at code-level with human labels versus 33% for baseline (Chen et al., 26 Sep 2025).

4. Illustrative Examples from Current Literature

Psychopathology Taxonomies

A recent paradigm is to operationalize mental health constructs using clinician-validated dimensional and transdiagnostic models. Exemplars include the SCID-5 and ADIS-5L for core internalizing/externalizing spectra, with dimensional summary scores, and latent variable approaches for p-factor modeling (Shani et al., 4 Apr 2025). Instruments such as SCL-90-R, CBCL/YSR, and PAI indices load onto higher-order factors, supporting both construct and predictive validity.

High-Dimensional Pain States

In "Definition and clinical validation of Pain Patient States" (Reinen et al., 2022), 5 ordinal states (A–E) were derived from k-means clustering on questionnaire and actigraphy features, then ordered through domain expert adjudication. Correlation with ODI (disability) and EQ-5D scales established validity and provided an interpretable, actionable taxonomy for real-time chronic pain tracking.

Pain State	Pain Score	Mobility	Mood	Sleep (h)	Clinical Meaning
A (Optimal)	≤2	≥3.5	≥4.2	≥7.5	Best health
B (Mild)	2–3	3.0–3.5	3.8–4.1	7–7.5	Mild symptoms
C (Intermediate)	3.5–4.5	2.5–3.0	3.0–3.5	6.5–7.0	Intermediate
D (Moderate/Severe)	5–6	2.0–2.5	2.5–3.0	≈6.0	Worsening
E (Severe)	≥6	≤2.0	≤2.5	≤5.5	Worst health

Error, Task, and Risk Taxonomies

• Clinical task taxonomies: MedHELM taxonomy spans 5 domains, 22 subcategories, 121 atomic tasks, validated with diverse, practicing clinicians and mapped to exhaustive coverage of real-world clinical operations (Bedi et al., 26 May 2025).
• Error ontology in LLM outputs: 59 granular error codes across 5 domains (Accessibility, Bias, Clinical Reasoning, Communication, Privacy) are adjudicated by board-certified physicians, validated for reliability, and used in retrieval-augmented LLM guardrails (Chen et al., 26 Sep 2025).
• Conversational safety/risk taxonomy: Immediate (suicidality, violence, decompensation) and potential risk (symptom exacerbation, engagement drop, alliance rupture) are mapped to DSM/NEQ/UE-ATR constructs and operationalized for real-time and simulation-based safety benchmarking (Steenstra et al., 21 May 2025).

5. Implementation Workflows and Practical Use

Implementation of clinician-validated taxonomies involves:

• Instrument/Tool selection: Priority to structured interviews and validated dimensional scales, supported by rater certification protocols and periodic retraining (Shani et al., 4 Apr 2025).
• Data collection: Standardized administration (in-person or telehealth), reliable data capture, and strict adherence to reliability and calibration procedures.
• Modeling and deployment: Latent-variable modeling for score extraction, nearest-centroid assignment for state taxonomies (as in k-means pain state mapping), and real-time flagging (as in risk/guardrail frameworks).
• Clinical integration and actionability: Taxonomic states are mapped to alert thresholds, dashboard summaries, or direct intervention triggers (e.g., state transitions prompting outreach in chronic pain; risk flags in conversational agents) (Reinen et al., 2022, Steenstra et al., 21 May 2025).

6. Advantages, Limitations, and Solutions

Advantages:

• Measures map directly to clinical constructs, maximizing interpretability and translational relevance.
• Dimensional/transdiagnostic architectures improve capture of subthreshold, comorbid, and dynamic presentations.
• Formal validation ensures statistical power, generalizability, and practical applicability.

Limitations:

• Labor and expertise requirements for gold-standard clinician labeling limit sample sizes.
• Inter-rater drift and model complexity (particularly for high-dimensional/hierarchical taxonomies) require ongoing recalibration and large datasets.
• Privacy/security considerations in sharing clinician-validated, potentially identifiable data.

Proposed solutions include hybrid data collection (nested clinician-labeled "gold" subsets for large proxy-labeled corpora), periodic rater refreshers, release of de-identified feature datasets, and public–private collaboration for resource and protocol standardization (Shani et al., 4 Apr 2025).

7. Summary Table of Clinician-Validated Taxonomy Applications

Application Domain	Taxonomy Type / Structure	Source (arXiv ID)
Psychopathology	Dimensional/transdiagnostic (SCID-5, HiTOP, p-factor)	(Shani et al., 4 Apr 2025)
Chronic Pain	Unsupervised k-means; 5-state, ordinal, trajectory-based	(Reinen et al., 2022)
Risk in Psychotherapy	Immediate and potential risk, multi-level, DSM/NEQ-aligned	(Steenstra et al., 21 May 2025)
Clinical NLP Errors	5 domains, 59 error codes, inductive coding + clinician adjudication	(Chen et al., 26 Sep 2025)
Medical Tasks (LLM)	Three-level (5 → 22 → 121), validated by 29 clinicians	(Bedi et al., 26 May 2025)
Psychiatric CSA	Three-way (accept/defer/reject), hybrid weighting/ranking	(Wang et al., 2022)
Mental Health Benchmarking	5 domains for real-world task coverage, expert consensus, ambiguous label capture	(Lamparth et al., 22 Feb 2025)

8. Impact and Future Directions

Clinician-validated taxonomies have become foundational for task definition, benchmarking, model evaluation, and safety assurance in computational health. They support not only the interpretability and clinical actionability of sophisticated machine learning models but also ensure these models faithfully reflect established clinical consensus and operational priorities. As computational methods and AI integration in healthcare advance, the expansion, rigorous maintenance, and federated application of such taxonomies remain central to translational impact and safe, equitable deployment.