ICD-11 “by the people for the people”: The open feedback proposal platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it