Quality of care indicators and their related outcomes: A population-based study in prostate cancer patients treated with radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: We evaluated the validity of 8 quality of care indicators for prostate cancer patients treated curatively with radical prostatectomy (RP) by examining their association with indicator-relevant outcomes. METHODS: We conducted a population-based retrospective cohort study of 646 prostate cancer patients diagnosed between 1990 and 1998 who received RP within 6 months of diagnosis. Data were collected from treating charts and linked to registry and administrative data. Quality indicators included: hospital volume, pre-treatment risk assessment, consultation with a radiation oncologist, appropriate follow-up care, nerve-sparing surgery, units of blood transfused, surgical margin status, and pelvic lymph node dissection during RP. Indicator-relevant outcomes were selected a priori by clinical members of the research team. The associations between indicators and their relevant outcomes were analyzed using regression techniques, to control for potential confounders. RESULTS: Of the quality indicators evaluated, only hospital volume was statistically significantly associated with the gradient in the expected direction. Patients treated in the lowest-volume hospitals (<1 RP/month) had lower cause-specific survival rates compared to patients treated in the highest-volume hospitals (≥7 RP/month) (HR=4.71 95%; CI 1.06-20.82). Completeness of follow-up care was associated with cause-specific survival but in the opposite direction to our hypothesis. CONCLUSION: The structural indicator of hospital volume was associated with cause-specific survival in accordance with our a priori hypothesis. Our negative findings for completeness of follow-up care call its validity into question. Issues of statistical power and measurement accuracy may have affected our validation of the remaining indicators underscoring the challenges in assessing the impact of accepted quality indicators.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".