ICD-11 “by the people for the people”: The open feedback proposal platform
Bibliographic record
Abstract
BACKGROUND: ICD-11's digital architecture and granularity distinguish it from previous revisions and expand its applicability beyond mortality statistics and public health. The official ICD-11 version is updated annually. However, a separate online Maintenance Platform is continuously updated and hosts the Proposal Platform: a novel online tool that enables interested parties from all over the world to contribute to ICD-11 content. Anyone can register on the Platform to propose updates, such as adding new medical terms or improving existing descriptions, helping keep the classification relevant and inclusive. As a public, transparent system, users can view or comment on other users' proposals. Proposals are carefully reviewed by expert WHO committees through a transparent, multi-step process that ensures scientific accuracy and consistency. High-priority updates, like emerging health conditions, can be fast-tracked for quicker inclusion. Once a proposal is accepted, it becomes effective in the following update. A clear justification is provided for rejected proposals. Since ICD-11 came into effect, most suggestions from users have been successfully implemented. OBJECTIVE: This article describes the proposal submission process, the rigorous proposal review process, and the roles of the WHO reference groups and committees involved. CONCLUSION: ICD-11 is a free, digital global health classification that anyone can help improve by submitting proposals through an open, transparent platform.Implications for health information management practice:This inclusive system empowers users worldwide to shape ICD-11 to reflect the evolving real-world medical and public health practice and emerging needs. This also prevents the need for country-specific modifications, ultimately improving the comparability of clinical data at the international level.
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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 | 0.359 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.058 | 0.044 |
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".