Availability of Data When Heart Failure Patients Are Admitted to Hospice
Bibliographic record
Abstract
Guidelines recommend hospice care for patients with advanced heart failure (HF) who are approaching end of life. However, little is known about the data available when HF patients are admitted to hospice. This pilot study surveyed the staff from 100 hospices in the United States and Canada about how frequently data were provided to or obtained by the hospice when admitting HF patients and how important they perceived the data. The survey response rate was 66%. Overall, data were less often provided or obtained than rated important (aggregate mean difference, P<.001, r=.75). Data important to prognostication or ongoing care were provided to or readily obtained by the hospices for blood pressure (50.0%), left ventricular ejection fraction (EF) (50.0%), edema (58.3%), HF medications, symptoms of dyspnea (63.2%), chest pain (57.2%), common comorbidities, and pacemaker (69.6%) or other devices (60.0%). Approximately half of the time, hospices reported that they rarely or never received information about medication intolerance. Significant amounts of clinically valid data are not provided to or obtained by hospice providers when admitting HF patients. Investigations are needed to corroborate these findings, understand information transfer at transitions in care, and to determine the impact of admission data on hospice care for HF patients. Congest Heart Fail.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".