The Effect of Institution Teaching Status on Perioperative Outcomes After Robotic Partial or Radical Nephrectomy
Bibliographic record
Abstract
OBJECTIVES: To test rates over time of robotic partial and radical nephrectomy (RPN and RRN) at teaching vs nonteaching institutions and to examine associated complication rates and length of stay. MATERIALS AND METHODS: Within the National Inpatient Sample (2008-2013), after stratification according to institutional teaching status, we examined the rates of robotic, open, and laparoscopic PN and RN. Subsequently, we tested complication rates and length of stay associated with RPN or RRN according to institutional teaching status. We relied on estimated annual percentage change (EAPC) with the least squares linear regression to test temporal trends and on multivariable logistic regression (MLR) and Poisson regression models to test complication rates and length of stay. RESULTS: Overall, 4070 and 1683 RPN and RRN were identified. In MLR, RPN rates were lower at teaching vs nonteaching institutions (odds ratio [OR]: 0.79, p < 0.001). RPN increased at similar rates over time at teaching and nonteaching institutions (EAPC: +32.1% and +30.1%, all p < 0.05). In MLR, RRN rates were similar at teaching and nonteaching institutions (p: 0.4). RRN rate increase over time was of similar magnitude at teaching vs nonteaching institutions (EAPC: +35.5% and +43.0%, all p < 0.05). RPN at teaching institutions yielded higher genitourinary complication rates (OR: 1.46, p: 0.01). RRN at teaching institutions resulted in lower respiratory complications (OR: 0.66, p: 0.04) and shorter length of stay (rate ratio [RR]: 0.93, p: 0.01), but higher intraoperative complication rates (OR: 3.39, p: 0.04). CONCLUSION: Despite statistically significant differences in rates of RPN use, according to the institutional teaching status and despite statistically significant difference in selected complications, no meaningful differences distinguish teaching vs nonteaching institution when RPN and RRN are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".