Critical care therapy use after radical cystectomy in patients with non-metastatic bladder cancer
Bibliographic record
Abstract
To assess critical care therapy use in patients with non-metastatic bladder cancer undergoing radical cystectomy. Using the National Inpatient Sample (2000-2019), we identified non-metastatic bladder cancer patients undergoing radical cystectomy. Study endpoints consisted of critical care therapy use, defined as total parenteral nutrition, invasive mechanical ventilation, dialysis, percutaneous endoscopic gastrostomy tube insertion, tracheostomy, and in-hospital mortality. Estimated annual percentage changes (EAPC) and multivariable logistic regression models were used. Of 25,535 patients, 3,091 (12.1%) received critical care therapy. Critical care therapy use decreased from 13.1 in 2000 to 5.9% in 2019 (EAPC -2.4; p=0.005), and in-hospital mortality also decreased from 3.4 to 0.7% (EAPC -4.2%; p<0.001). Older (≥80 years: odds ratio [OR] 1.91; p<0.001, and 60-79 years OR 1.41; p<0.001) and sicker patients (Charlson comorbidity index [CCI] ≥3: OR 3.16; p<0.001; CCI 1-2 OR 1.89; p<0.001) were more likely to receive critical care therapy. Conversely, minimally-invasive surgical approach (OR 0.66; p=0.01) and teaching hospital status (OR 0.70; p=0.008) independently predicted lower critical care therapy use. The same risk factors were identified for in-hospital mortality. Critical care therapy use in radical cystectomy patients decreased from 13.1 in 2000 to 5.9% in 2019 and so did in-hospital mortality (3.4 to 0.7%). Of all critical care therapy determinants, CCI ≥3 (OR 3.2) represented the strongest followed by octogenarian age (OR 1.9). Even after adjustment for patient age and comorbidities, minimally-invasive surgical approach and teaching hospital status were associated with lower critical care therapy use, and lower in-hospital mortality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".