Monitoring risk-adjusted medical outcomes allowing for changes over time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider the problem of monitoring and comparing medical outcomes, such as surgical performance, over time. Performance is subject to change due to a variety of reasons including patient heterogeneity, learning, deteriorating skills due to aging, etc. For instance, we expect inexperienced surgeons to improve their skills with practice. We propose a graphical method to monitor surgical performance that incorporates risk adjustment to account for patient heterogeneity. The procedure gives more weight to recent outcomes and down-weights the influence of outcomes further in the past. The chart is clinically interpretable as it plots an estimate of the failure rate for a "standard" patient. The chart also includes a measure of uncertainty in this estimate. We can implement the method using historical data or start from scratch. As the monitoring proceeds, we can base the estimated failure rate on a known risk model or use the observed outcomes to update the risk model as time passes. We illustrate the proposed method with an example from cardiac surgery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it