Trends and Social Barriers for Inpatient Palliative Care in Patients With Metastatic Bladder Cancer Receiving Critical Care Therapies
Bibliographic record
Abstract
BACKGROUND: Use of inpatient palliative care (IPC) in the treatment of advanced cancer represents a well-established guideline recommendation. A recent analysis showed that patients with genitourinary cancer benefit from IPC at the second lowest rate among 4 examined primary cancers, namely lung, breast, colorectal, and genitourinary. Based on this observation, temporal trends and predictors of IPC use were examined in patients with metastatic urothelial carcinoma of the bladder (mUCB) receiving critical care therapies (CCTs). PATIENTS AND METHODS: Patients with mUCB receiving CCTs were identified within the Nationwide Inpatient Sample database (2004-2015). IPC use rates were evaluated in estimated annual percentage change (EAPC) analyses. Multivariable logistic regression models with adjustment for clustering at the hospital level were used. RESULTS: Of 1,944 patients with mUCB receiving CCTs, 191 (9.8%) received IPC. From 2004 through 2015, IPC use increased from 0.7% to 25.0%, respectively (EAPC, +23.9%; P<.001). In analyses stratified according to regions, the highest increase in IPC use was recorded in the Northeast (EAPC, +44.0%), followed by the West (EAPC, +26.8%), South (EAPC, +22.9%), and Midwest (EAPC, +15.5%). Moreover, the lowest rate of IPC adoption in 2015 was recorded in the Midwest (14.3%). In multivariable logistic regression models, teaching status (odds ratio [OR], 1.97; P<.001), more recent diagnosis (2010-2015; OR, 3.89; P<.001), and presence of liver metastases (OR, 1.77; P=.02) were associated with higher IPC rates. Conversely, Hispanic race (OR, 0.42; P=.03) and being hospitalized in the Northeast (OR, 0.36; P=.01) were associated with lower rate of IPC adoption. Finally, patients with a primary admission diagnosis that consisted of infection (OR, 2.05; P=.002), cardiovascular disorders (OR, 2.10; P=.03), or pulmonary disorders (OR, 2.81; P=.005) were more likely to receive IPC. CONCLUSIONS: The rate of IPC use in patients with mUCB receiving CCTs sharply increased between 2004 and 2015. The presence of liver metastases, infections, or cardiopulmonary disorders as admission diagnoses represented independent predictors of higher IPC use. Conversely, Hispanic race, nonteaching hospital status, and hospitalization in the Midwest were identified as independent predictors of lower IPC use and represent targets for efforts to improve IPC delivery in patients with mUCB receiving CCT.
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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 | 0.001 | 0.006 |
| 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) | 0.002 | 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".