Prakriti (constitutional typology) in Ayurveda: a critical review of Prakriti assessment tools and their scientific validity
Bibliographic record
Abstract
Background: Prakriti or constitutional typology is the foundation of personalized health care in Ayurveda. Traditionally, Ayurvedic clinicians have assessed Prakriti in a primarily experience-based and often subjective manner. However, in the past few decades attempts to develop objective tools have been made by researchers from multidisciplinary domains. This review aimed to identify existing Ayurvedic Prakriti assessment tools and evaluate their scientific rigor. Methods: Aligned with the SANRA framework, our narrative review incorporated systematic elements. A Boolean search in PubMed, Scopus, and Cochrane in November 2024 using ("Prakriti") AND ("Ayurveda" OR "Ayurvedic") yielded 635 articles, together with 12 additional articles from citations search. Ninety four studies met the inclusion criteria. Prakriti assessment tools were quantified and evaluated using Scale Development and Validation Framework by Boateng et al., alongside custom set of study quality indicators to assess their methodological rigor. Results: Between 1987 and 2024, 64 unique Prakriti assessment tools (PATs) were identified, each using one or more methods to perform data collection and decision-making tasks. Variations in the selection and application of these methods resulted in the development of diverse methodological frameworks for Prakriti assessment. Of the 64 PATs identified, only 20 PATs underwent any form of validation and among them, just two PATs, the CCRAS-PAS software and ACPI scale met seven of the nine recommended criteria. Most tools lacked dimensionality testing, test-retest reliability, contextual validity and were not tested across diverse populations, indicating a high risk of developer-bias. Additionally, 32 categories of measurable correlates to Prakriti have been studied across 94 studies, but only five of them were studied using validated tools. Conclusion: Much progress has been made in developing methodology and integrating technology for creating Prakriti assessment tools along with attempts to identifying measurable correlates to Prakriti that could potentially serve as Prakriti biomarkers. Currently no tool fully meets the evaluation criteria of the Scale Development and Validation framework, except CCRAS-PAS and ACPI that show partial readiness and can be refined. Further work is needed to establish Prakriti as a clinically validated measurable construct and to integrate Ayurveda into the domain of personalized health care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".