The Use of a Palliative Care Screening Tool to Improve Referrals to Palliative Care Services in Community-Based Hospitals
Bibliographic record
Abstract
Despite efforts to improve access to palliative care services, a significant number of patients still have unmet needs throughout their continuum of care. As such, this project was conducted to increase recognition of patients who could benefit from palliative care, increase referrals, and connect regional sites. This study utilized Plan-Do-Study-Act cycles through a quality improvement approach to develop and test the Palliative Care Screening Tool and aimed to screen 100% of patients within 24 hours who were admitted to selected units by February 2017. The intervention was implemented in 3 different units, each within community hospitals. Patients 18 years or older were screened if they were admitted to one of the selected units for the project, regardless of their diagnosis, age, or comorbidities. The percentage of newly admitted patients who were screened and the total number of palliative care consults were assessed as outcome measures. The tool was met with varying compliance among the 3 sites. However, there was an overall increase in consults across all hospital sites, and an increase in the proportion of noncancer patients was demonstrated. Although the aim was not reached, the tool helped to create a shift in the demographic of patients identified as palliative.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".