Expanding the Learning Health Care System Beyond the Academic Health Center
Bibliographic record
Abstract
1Research fellow, Department of Medicine Clinician Investigator Program, University of British Columbia; and research fellow, Post-COVID-19 Interdisciplinary Clinical Care Network, Provincial Health Services Authority, Vancouver, British Columbia, Canada; email: [email protected]; ORCID: https://orcid.org/0000-0002-5193-4618. 2Program manager, Post-COVID-19 Interdisciplinary Clinical Care Network, Provincial Health Services Authority, Vancouver, British Columbia, Canada. 3Professor and head, Division of Nephrology, Department of Medicine, University of British Columbia; senior medical lead, Integration Clinical and Academic Networks, Providence Health Care; executive director, BC Renal; and director, Post-COVID-19 Interdisciplinary Clinical Care Network, Provincial Health Services Authority, Vancouver, British Columbia, Canada. Acknowledgements: The authors would also like to thank the patients, clinicians, researchers, and staff who have supported the Post-COVID-19 Interdisciplinary Clinical Care Network. Funding Support: H. Naik is supported by the University of British Columbia Clinician Investigator Program, and research activities at the Post-COVID-19 Interdisciplinary Clinical Care Network have been supported by Michael Smith Health Research BC. Other Disclosures: None reported. Ethical Approval: Reported as not applicable. First published online.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.032 | 0.024 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".