Use of inpatient palliative care in metastatic testicular cancer patients undergoing critical care therapy: insights from the national inpatient sample
Bibliographic record
Abstract
To test for rates of inpatient palliative care (IPC) in metastatic testicular cancer patients receiving critical care therapy (CCT). Within the Nationwide Inpatient Sample (NIS) database (2008-2019), we tabulated IPC rates in metastatic testicular cancer patients receiving CCT, namely invasive mechanical ventilation (IMV), percutaneous endoscopic gastrostomy tube (PEG), dialysis for acute kidney failure (AKF), total parenteral nutrition (TPN) or tracheostomy. Univariable and multivariable logistic regression models addressing IPC were fitted. Of 420 metastatic testicular cancer patients undergoing CCT, 70 (17%) received IPC. Between 2008 and 2019, the rates of IPC among metastatic testicular cancer patients undergoing CCT increased from 5 to 19%, with the highest rate of 30% in 2018 (EAPC: + 9.5%; 95% CI + 4.7 to + 15.2%; p = 0.005). IPC patients were older (35 vs. 31 years, p = 0.01), more frequently had do not resuscitate (DNR) status (34 vs. 4%, p < 0.001), more frequently exhibited brain metastases (29 vs. 17%, p = 0.03), were more frequently treated with IMV (76 vs. 53%, p < 0.001) and exhibited higher rate of inpatient mortality (74 vs. 29%, p < 0.001). In multivariable analyses, DNR status (OR 10.23, p < 0.001) and African American race/ethnicity (OR 4.69, p = 0.003) were identified as independent predictors of higher IPC use. We observed a significant increase in rates of IPC use in metastatic testicular cancer patients receiving CCT, rising from 5 to 19% between 2008 and 2019. However, this rates remain lower compared to metastatic lung cancer patients, indicating the need for further awareness among clinicians treating metastatic testicular cancer. The increase in IPC rates for metastatic testicular cancer patients receiving CCT indicates a need for ongoing education and awareness among healthcare providers. This could enhance the integration of IPC in the treatment of advanced cancer, potentially improving quality of life and care outcomes for survivors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".