Trends and Disparities in Inpatient Palliative Care Use in Metastatic Renal Cell Carcinoma Patients Receiving Critical Care Therapy
Bibliographic record
Abstract
PURPOSE: Temporal trends in and predictors of inpatient palliative care use in patients with metastatic renal cell carcinoma (mRCC) undergoing critical care therapy are unknown. METHODS: Relying on the National Inpatient Sample (2008-2019), we identified mRCC patients undergoing critical care therapy, namely invasive mechanical ventilation, percutaneous endoscopic gastrostomy tube insertion, dialysis for acute kidney failure, total parenteral nutrition, or tracheostomy. Estimated annual percentage changes (EAPC) analyses and multivariable logistic regression models addressed inpatient palliative care use. RESULTS: Of 3802 mRCC patients undergoing critical care therapy, 817 (21.5%) received inpatient palliative care. Overall, inpatient palliative care use increased from 4.9% to 31.5% between 2008 and 2019 (EAPC +9.2%). In subgroup analyses, the highest increase in inpatient palliative care use was observed in the Midwest (EAPC: +11.9%), in the South (EAPC +10.4%), and in teaching hospitals (EAPC +9.0%; all P ≤ .004). In logistic regression models, teaching hospital status (odds ratio [OR] 1.41) and contemporary year interval (OR 2.12; all P < .001) independently predicted higher inpatient palliative care rates. Conversely, hospital admission in the Northeast (OR 0.53) or in the South (OR 0.79; all P ≤ .03) was associated with lower inpatient palliative care rates than in the West. CONCLUSION: In mRCC patients, inpatient palliative care rates have improved over time, with the highest increase in hospitals in the Midwest and in the South. Moreover, admission to teaching hospitals or in the West is associated with higher inpatient palliative care rates. In consequence, regional disparities, as well as differences according to teaching hospital status represent targets to achieve comprehensive inpatient palliative care coverage in mRCC patients receiving critical care therapy.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".