Does early palliative identification improve the use of palliative care services?
Bibliographic record
Abstract
PURPOSE: To evaluate whether the early identification of patients who may benefit from palliative care impacts on the use of palliative, community and acute-based care services. METHODS: Between 2014 and 2017, physicians from eight sites were encouraged to systematically identify patients who were likely to die within one year and would were thought to benefit from early palliative care. Patients in the INTEGRATE Intervention Group were 1:1 matched to controls selected from provincial healthcare administrative data using propensity score-matching. The use of palliative care, community-based care services (home care, physician home visit, and outpatient opioid use) and acute care (emergency department, hospitalization) was each evaluated within one year after the date of identification. The hazard ratio (HR) in the Intervention Group was calculated for each outcome. RESULTS: Of the 1,185 patients in the Intervention Group, 951 (80.3%) used palliative care services during follow-up, compared to 739 (62.4%) among 1,185 patients in the Control Group [HR of 1.69 (95% CI 1.56 to 1.82)]. The Intervention Group also had higher proportions of patients who used home care [81.4% vs. 55.2%; HR 2.07 (95% CI 1.89 to 2.27)], had physician home visits [35.5% vs. 23.7%; HR 1.63 (95% CI 1.46 to 1.92)] or had increased outpatient opioid use [64.3% vs. 52.1%); HR 1.43 (95% CI 1.30 to 1.57]. The Intervention Group was also more likely to have a hospitalization that was not primarily focused on palliative care (1.42 (95% CI 1.28 to 1.58)) and an unplanned emergency department visit for non-palliative care purpose (1.47 (95% CI 1.32 to 1.64)). CONCLUSION: Physicians actively identifying patients who would benefit from palliative care resulted in increased use of palliative and community-based care services, but also increased use of acute care services.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".