Use of inpatient palliative care in metastatic urethral cancer
Bibliographic record
Abstract
BACKGROUND: In metastatic urethral cancer, temporal trends, and patterns of inpatient palliative care (IPC) use are unknown. METHODS: Relying on the National Inpatient Sample (2006-2019), metastatic urethral cancer patients were stratified according to IPC use. Estimated annual percentage changes (EAPC) analyses and multivariable logistic regression models (LRM) for the prediction of IPC use were fitted. RESULTS: Of 1,106 metastatic urethral cancer patients, 199 (18%) received IPC. IPC use increased from 5.8 to 28.0% over time in the overall cohort (EAPC +9.8%; P < 0.001), from <12.5 to 35.1% (EAPC +11.2%; P < 0.001), and from <12.5 to 24.7% (EAPC +9.4%; P = 0.01) in respectively females and males. Lowest IPC rates were recorded in the Midwest (13.5%) vs. highest in the South (22.5%). IPC patients were more frequently female (44 vs. 37%), and more frequently exhibited bone metastases (45 vs. 34%). In multivariable LRM, female sex (multivariable odds ratio [OR] 1.46, 95% confidence interval [CI] 1.05-2.02; P = 0.02), and bone metastases (OR 1.46, 95%CI 1.02-2.10; P = 0.04) independently predicted higher IPC rates. Conversely, hospitalization in the Midwest (OR 0.53, 95%CI 0.31-0.91; P = 0.02), and in the Northeast (OR 0.48, 95%CI 0.28-0.82; P = 0.01) were both associated with lower IPC use than hospitalization in the West. CONCLUSION: IPC use in metastatic urethral cancer increased from a marginal rate of 5.8% to as high as 28%. Ideally, differences according to sex, metastatic site, and region should be addressed to improve IPC use rates.
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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) | 0.002 | 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".