Problems for clinical judgement: 4. Surviving in the report card era.
Bibliographic record
Abstract
Health care report cards involve comparisons of health care systems, hospitals or clinicians on performance measures. They are going to be an important feature of medical care in Canada in the new millennium as patients demand more information about their medical care. Although many clinicians are aware of this growing trend, they may not be prepared for all of its implications. In this article, we provide some historical background on health care report cards and describe a number of strategies to help clinicians survive and thrive in the report card era. We offer a number of tips ranging from knowing your outcomes first to proactively getting involved in developing report cards.
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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 | 0.154 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".