Temporal Trends and Differences in Inpatient Palliative Care Use in Metastatic Penile Cancer Patients
Bibliographic record
Abstract
Objectives: To quantify inpatient palliative care use over time and to test whether patient or hospital characteristics represent determinants of inpatient palliative care use in patients with metastatic penile cancer. Methods: Relying on the National Inpatient Sample database (2006–2019), we identified 1017 metastatic penile cancer patients. Estimated annual percentage change analyses and multivariable logistic regression models addressing inpatient palliative care use were fitted. Results: Of 1017 metastatic penile cancer patients, 139 (13.7%) received inpatient palliative care. Over time, the proportion of inpatient palliative care use per year increased from 6.5% in 2006 to 17.8% in 2019 (estimated annual percentage change +6.7%; p = 0.001). In the multivariable logistic regression models, contemporary study years (odds ratio [OR] 1.80; p = 0.003), the presence of bone metastases (OR 1.90; p = 0.002) and the presence of brain metastases (OR 2.60; p = 0.013) independently predicted higher inpatient palliative care use. Conversely, distant lymph node metastases independently predicted lower inpatient palliative care use (OR 0.58; p = 0.022). Finally, hospital admission in the South (OR 2.42; p = 0.007) and in the Northeast (OR 2.34; p = 0.015) was associated with higher inpatient palliative care use than hospital admission in the Midwest. Conclusions: In metastatic penile cancer patients, the proportions of inpatient palliative care use were low but have increased over time. Unfortunately, some geographical regions are more refractory to inpatient palliative care use than others. Finally, specific patient characteristics such as bone metastases and brain metastases represent independent predictors of higher inpatient palliative care use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".