Assessment of healthcare quality metrics: length-of-stay, 30-day readmission, and 30-day mortality for radical nephrectomy with inferior vena cava thrombectomy
Bibliographic record
Abstract
INTRODUCTION: Length-of-stay (LOS), 30-day readmission, and 30-day mortality are metrics used to assess quality of care and provider reimbursement. Therefore, we investigated patient- and hospital-level characteristics associated with the three healthcare quality metrics for radical nephrectomy with inferior vena cava (IVC) thrombectomy. METHODS: Using the National Cancer Data Base, we established a cohort of patients who received radical nephrectomy following the diagnosis of renal cell carcinoma (RCC) stage cT3b between 1998 and 2011. We then assessed the associations between patient- or hospital-level characteristics and LOS using multivariable negative binomial regression. We used multivariable logistic regression to determine the associations between the characteristics and 30-day readmission or 30-day mortality. RESULTS: During the study period, 5768 patients were diagnosed with RCC stage cT3b and underwent radical nephrectomy. LOS ≤2 days and ≥9 days were associated with a higher likelihood of 30-day readmission (respective odds ratio [OR] 1.61 and 1.58) and 30-day mortality (respective OR 11.62 and 11.87). Older patients (60-79 years vs. <50 years) were less likely to experience 30-day readmission (OR 0.46-0.52). Older patients (≥80 years vs. <50 years, OR 3.67) and patients with a high index of comorbidity (Charlson comorbidity score ≥ 2 vs. 0, OR 1.95) were more likely to suffer 30-day mortality. CONCLUSIONS: LOS is an important predictor of short-term readmission and mortality following radical nephrectomy with IVC thrombectomy. Older age and a high index of comorbidity also predict short-term mortality after the surgery.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".