Commentary – From Mixtapes to Playlists: Evolving Options for Capturing Diagnoses in Canadian Physicians’ Data
Bibliographic record
Abstract
Physician billing claims are rich sources of administrative health data.However, diagnostic codes in billing claims are drawn from the International Classification of Diseases, Ninth Revision (WHO & International Conference for the Ninth Revision of the International Classification of Diseases 1977), which has not been updated by the World Health Organization in three decades.With its updated and expanded content and its digital tooling, the International Classification of Diseases 11th Revision (ICD-11) (WHO n.d.a.) could be considered for this purpose.Primary care practitioners have always found the ICD inadequate for their needs.This may change with ICD-11, with which the International Classification of Primary Care (ICPC) (van Boven and Ten Napel 2021) is more closely aligned.ICD-11, ICPC and the Systematized Nomenclature of Medicine Clinical Terms present evolving options for capturing diagnoses in physician data. RésuméLes demandes de paiement faites par les médecins constituent de riches sources de données administratives sur la santé.Cependant, les codes de diagnostic utilisés pour ces demandes proviennent de la Classification internationale des maladies, neuvième révision (WHO & International Conference for the Ninth Revision of the International Classification of DISCUSSION AND DEBATE
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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 | 0.019 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.060 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".