Validation of quality indicators for radical prostatectomy
Bibliographic record
Abstract
The feasibility and validity of proposed radical prostatectomy quality indicators has not been well studied. We assessed indicator availability from treating charts. We tested the convergent construct validity of a modified subset that were available from this information source by correlating them to hospital prostatectomy volume, a variable repeatedly associated with the quality of surgical care. The study population consisted of a stratified random sample of prostate cancer patients who were: (i) diagnosed between 1990 and 1998 in Ontario and (ii) treated by radical prostatectomy with curative intent within 6 months of diagnosis (n = 645). Of the 9 candidate quality indicators assessed, 4 were missing for 25-56% of study subjects and were not analyzed further. We discuss the implications of this missing information on feasibility of their use. For blood transfusions of 3 units or greater, length of hospital stay and use of non-nerve-sparing surgical technique, worse outcomes were generally apparent with decreasing hospital volume. Acute complication rates and positive surgical margin rates did not increase with decreasing hospital volume. We were able to demonstrate convergent construct validity for 3 quality indicators. Upon further validation, this readily available information may be applied to aid providers and quality councils to more effectively identify problems and guide change in the management of early prostate cancer.
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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 | 0.071 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".