Defining Practice Populations For Primary Care: Methods and Issues
Bibliographic record
Abstract
iACKNOWLEDGMENTS The authors wish to acknowledge the contributions of the many individuals whose efforts and expertise made it possible to produce this report. We thank the following individuals: Leonard MacWilliam for programming support regarding the Ambulatory Diagnostic Groups; Carolyn DeCoster, Deborah Nowicki, Evelyn Shapiro, and Fred Toll for providing feedback on a draft version of this report; and the members of the Primary Care Unit, especially Avis Gray, Bill MacKeen, Kathy Mestery, and David Patton for their input into this report. Special thanks go to Stephen Gray, Brian Hutchison, and Jennifer Gait for their detailed and thoughtful reviews. We also acknowledge the help of Carole Ouelette in the preparation of this manuscript. We acknowledge the Faculty of Medicine Research Ethics Board and the Access and Confidentiality Committee of Manitoba Health for their thoughtful review of this project. Strict policies and procedures to protect the privacy and security of data have been followed in producing this report. The results and conclusions are those of the authors and no official endorsement by Manitoba Health was intended nor should be inferred. This report was prepared at the request of Manitoba Health as part of the contract between
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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 | 0.623 | 0.711 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".