Active identification of patients appropriate for palliative care: Impact on use of palliative care and home care resources.
Bibliographic record
Abstract
101 Background: This research evaluates whether active identification of patients who may benefit from a palliative approach to care changes the use of palliative care and home care services. Methods: Between 2014 and 2017, Cancer Care Ontario implemented the INTEGRATE project at 4 cancer centres and 4 primary care teams. Physicians in participating sites were encouraged to systematically identify patients who were likely to die within 1 year and would benefit from a palliative approach to care. Patients in the INTEGRATE intervention group were 1:1 matched to non-intervention controls selected from provincial healthcare administrative data based on a publicly funded health system using the propensity score-matching. Palliative care and home care services utilization was evaluated within 1 year after the date of identification (index date), censoring on death, or March 31, 2017, the study end date. Cumulative incidence function was used to estimate the probability of having used care services, with death as a competing event. Rate of service use per 360 patient-days was calculated. Analyses were done separately for palliative care and home care. Results: Of the 1,187 patients in the INTEGRATE project, 1,185 were matched to a control. The intervention and the control groups were well-balanced on demographics, diagnosis, comorbidities, and death status. The probability of using palliative services in the intervention group was 80.3%, which was significantly higher than that in the control group (62.4%) with more palliative care visits in the intervention group [29.7 (95%CI: 29.4 to 30.1] per 360 patient-days) than in the control group [19.6 (95%CI: 19.3 to 19.9) per 360 patient-days]. The intervention group had a greater probability of receiving home care (81.4%) than the control group (55.2%) with more homecare visits per 360 patient-days [64.7 (95%CI: 64.2 to 65.3) vs. 35.3 (95%CI: 34.9 to 35.7)] The intervention group also had higher physician home visits as compared to the control group (36.5% vs. 23.7%). Conclusions: Physicians actively identifying patients that would benefit from palliative care resulted in increased use of palliative care and home 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".