Preventable mortality after common urological surgery: failing to rescue?
Bibliographic record
Abstract
OBJECTIVE: To assess in-hospital mortality in patients undergoing many commonly performed urological surgeries in light of decreasing nationwide perioperative mortality over the past decade. This phenomenon has been attributed in part to a decline in 'failure to rescue' (FTR) rates, e.g. death after a complication that was potentially recognisable/preventable. PATIENTS AND METHODS: Discharges of all patients undergoing urological surgery between 1998 and 2010 were extracted from the Nationwide Inpatient Sample and assessed for overall and FTR mortality. Admission trends were assessed with linear regression. Logistic regression models fitted with generalised estimating equations were used to estimate the impact of primary predictors on over-all and FTR mortality and changes in mortality rates. RESULTS: Between 1998 and 2010, an estimated 7,725,736 urological surgeries requiring hospitalisation were performed in the USA; admissions for urological surgery decreased 0.63% per year (P = 0.008). Odds of overall mortality decreased slightly (odds ratio [OR] 0.990, 95% confidence interval [CI] 0.988-0.993), yet the odds of mortality attributable to FTR increased 5% every year (OR 1.050, 95% CI 1.038-1.062). Patient age, race, Charlson Comorbidity Index, public insurance status, as well as urban hospital location were independent predictors of FTR mortality (P < 0.001). CONCLUSION: A shift from inpatient to outpatient surgery for commonly performed urological procedures has coincided with increasing rates of FTR mortality. Older, sicker, minority group patients and those with public insurance were more likely to die after a potentially recognisable/preventable complication. These strata of high-risk individuals represent ideal targets for process improvement initiatives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".