Quality improvement through public reporting: The surgeon scorecard – are we there yet?
Bibliographic record
Abstract
As national healthcare reform continues to place greater emphasis on providing high value care, measures designed to track clinical performance remain relatively overlooked. To that extent, several organizations have attempted to create objective grading systems to evaluate orthopaedic surgeon quality and performance. While attempting to address these issues, ProPublica’s Surgeon Scorecard has provoked national debate among patient advocates and healthcare providers. The methodology behind the Scorecard was developed at the Harvard School of Public Health with an aim to provide a more robust means of comparing surgical performance and outcomes for patients and healthcare organizations. Currently, the Scorecard assesses eight elective surgical procedures, including total knee and hip arthroplasty, through the use of the Medicare Claims Dataset. The impact of the Scorecard on orthopaedic practice has yet to be established. In this discussion, we analyze the Scorecard from the perspective of various stakeholders to identify its benefits and shortcomings, as well as offer direction for further improvement.
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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 | 0.264 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".