Critical care therapy and in-hospital mortality after radical nephroureterectomy for nonmetastatic upper urinary tract carcinoma
Bibliographic record
Abstract
BACKGROUND: Use of critical care therapies (CCT), that include invasive mechanical ventilation (IMV), total parenteral nutrition (TPN) and other modalities are unknown after radical nephroureterectomy (RNU) for upper urinary tract carcinoma (UUTC). Their relationship with in-hospital mortality is also unknown. METHODS: Within the National Inpatient Sample (2008-2019), we identified non-metastatic UUTC patients treated with RNU. Multivariable logistic regression models were used. RESULTS: Of 8,995 patients, 375 (4.2%) received CCT and 82 (0.9%) experienced in-hospital mortality. Of CCT modalities, 215 (2.4%) received IMV and 139 (1.5%) TPN. Temporal CCT, IMV, and TPN trends very closely followed in-hospital mortality trends. Relative to historical UUTC patients (2008-2013), contemporary (2014-2019) patients exhibited lower CCT (Δ = 2.2%, P value < 0.0001), lower IMV (Δ = 1.4%, P < 0.0001), lower TPN (Δ = 2.2%, P < 0.0001), and lower in-hospital mortality (Δ = 0.4%, P = 0.03) rates. Of in-hospital mortalities, 52 out of 82 received CCT but 30 of 82 did not. Median age (> 72 years; odds ratio [OR] 1.4; P = 0.002) and Charlson comorbidity index ≥ 3 (OR 4.1; P < 0.001) and ≥ 1-2 (OR 1.7; P = 0.001) independently predicted overall higher CCT, IMV, TPN, and in-hospital mortality. CONCLUSION: After RNU, CCT rates parallels in-hospital mortality rates. CCT represents a 5 to 6-fold multiple of in-hospital mortality rate. In RNU patients, CCT rates are higher in older and sicker individuals. Lower CCT rates that are paralleled by lower in-hospital mortality may be interpreted as an indicator of improved quality of care. Ideally all in-hospital mortalities should be predated by CCT exposure.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".