Patterns of Palliative Care Referral in Patients Admitted With Heart Failure Requiring Mechanical Ventilation
Bibliographic record
Abstract
BACKGROUND: Palliative care is recommended for advanced heart failure (HF) by several major societies, though prior studies indicate that it is underutilized. AIM: To investigate patterns of palliative care referral for patients admitted with HF exacerbations, as well as to examine patient and hospital factors associated with different rates of palliative care referral. DESIGN: Retrospective nationwide cohort analysis utilizing the National Inpatient Sample from 2006 to 2012. Patients referred to palliative care were compared to those who were not. SETTING/PARTICIPANTS: Patients ≥18 years of age with a primary diagnosis of HF requiring mechanical ventilation (MV) were included. A cohort of non-HF patients with metastatic cancer was created for temporal comparison. RESULTS: Between 2006 and 2012, 74 824 patients underwent MV for HF. A referral to palliative care was made in 2903 (3.9%) patients. The rate of referral for palliative care in HF increased from 0.8% in 2006 to 6.4% in 2012 ( P < .01). In comparison, rate of palliative care referral in patients with cancer increased from 2.9% in 2006 to 11.9% in 2012 ( P < .01). In a multivariate logistic regression model, higher socioeconomic status (SES) was associated with increased access to palliative care ( P < .01). Racial differences were also observed in rates of referral to palliative care. CONCLUSION: The use of palliative care for patients with advanced HF increased during the study period; however, palliative care remains underutilized in this setting. Patient factors such as race and SES affect access to palliative care.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".