Incorporation of complementary and traditional medicine in ICD-11
Bibliographic record
Abstract
Traditional medicine (TM) is practiced in various forms in over 180 countries. Despite this, health information systems on TM are limited. Consistent with this, the World Health Organization's (WHO) international classification for diseases (ICD) has not to date included TM concepts. This is now changing, as the WHO has endorsed the reflection of TM paradigms in the new 11th Revision of ICD (ICD-11). Although some countries have had national Traditional Medicine classification systems for many years, information from such systems has not been standardized nor been made available globally. By including TM within the ICD, international standardization will be possible allowing for measuring, counting, comparing, formulating questions and monitoring over time. ICD-11 is a classification system for the twenty-first century, and it now provides an opportunity for interested users to integrate the coding of diagnostic concepts from both TM and Western Medicine. This paper describes the new TM classification in ICD and demonstrates through coding examples how to code TM concepts alongside Western Medicine concepts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".