Hospital-level quality indicators for kidney cancer surgery: A Veteran’s Affairs national health system validation of concept.
Bibliographic record
Abstract
667 Background: Validation and implementation of quality indicators (QIs) for oncological surgical care is imperative in national health care systems. However, QIs must be adjusted for significant case-mix variations among hospitals and to capture disparate patient outcomes. Here, we explore and validate a compound quality score (CQS) as a metric for hospital-level quality of care in kidney cancer patients. Methods: Kidney cancer patients (n = 8233) treated at the VA (2005-2015) were identified. Two previously described and validated process QIs were explored: the proportion of patients with a) T1a tumors undergoing partial nephrectomy; and b) T1-T2 tumors undergoing minimally invasive radical nephrectomy. Demographics, comorbidity, tumor characteristics and treatment year were used for case-mix adjustment using indirect standardization / multivariable regression models. The predicted vs observed ratio of cases was calculated to generate each QI score. CQS represents the sum of both QIs scores. Ninety-six hospitals were benchmarked by CQS and patient-level outcomes were regressed on CQS levels to assess for length of stay (LOS), 30 days complications/readmission, 90 days overall mortality and total cost of surgical admission. Results: CQS identified 25, 33 and 38 hospitals with higher, lower and average performance, respectively. Total CQS score was independently associated with LOS [β = -0.04, p< 0.01, predicted LOS 0.84 days shorter for CQS = 2 vs. CQS = -2], 30 days surgical complications [OR = 0.88, p < 0.01] or 30 days medical complications [OR = 0.93, p < 0.01] and total cost of surgical admission [β = -0.014, p< 0.01, predicted 12% lower cost for CQS = 2 vs. CQS = -2]. No association was found between CQS and 30 day readmissions or 90 days mortality (all p>0.05), although low event rates were observed (8.9% and 1.7%, respectively). Conclusions : Variability in quality of surgical care at a hospital-level can be captured with the CQS among kidney cancer patients. CQS is associated with length of stay, post-operative complications and total cost of surgical admission. Quality indicators should be used to identify, audit and implement quality improvement strategies across health systems.
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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 | 0.078 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".