Additional file 1 of Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study
Bibliographic record
Abstract
Additional file 1: U-Statistic Definition. Table S1. Summary of Hotelling’s T2 statistics for chronic health conditions in the transition periods. Figure S1. Goodness-of-fit statistics for negative binomial and Poisson regression models for 16 chronic health conditions. Figure S2. Chronic health conditions with significant changes in regression coefficients within the transition periods. Figure S3. Chronic health conditions with no significant changes in regression coefficients within the transition periods. Figure S4. Chronic health conditions with significant changes in regression model parameter estimates, physician billing claims. Figure S5. Chronic health conditions with no significant changes in regression model parameter estimates, physician billing claims. Figure S6. Chronic health conditions with significant changes in regression model parameter estimates, hospital records. Figure S7. Chronic health conditions with no significant changes in regression model parameter estimates, hospital records.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.775 | 0.122 |
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".