PP41 British columbia emergency health services assess, see treat and refer palliative clinical pathway
Bibliographic record
Abstract
Background/Research Objectives Paramedic services have experienced a steadily increasing demand from palliative patients accessing 911 during times of acute crisis, and not wishing subsequent conveyance to ED. Early data indicates that many of these patients are NOT already connected to palliative care teams. To address this demand and to connect patients to care, BCEHS introduced the Assess, See, Treat and Refer (ASTAR)-Palliative Clinical Pathway. Objectives are to reduce patient conveyance to ED, reduce hospitalizations and improve patient care through referral after non-conveyance. Intervention Paramedic activation of the ASTaR Palliative Clinical Pathway results in referral of non-conveyed palliative patients to local Home and Community Care teams and BCEHS paramedics. The referral occurs within 1-6 hours of paramedic contact and follow up occurs over the next 24-48 hours by telephone. This referral action provides safe, effective, patient-centred care for non-conveyed patients, and also fills a gap for connecting patients to local palliative care teams. Impact A retrospective case review of 183 cases was conducted. Symptom improvement was achieved in 70% of cases, the ED non-conveyance rate was 19%, and the time on task when palliative patients were treated at home and not conveyed was 37% less (52 minutes) than if palliative patients were transported (82 minutes). All 183 patients were connected to either the local home and community care team or BCEHS Rural Advanced Care Community Paramedics (RACCP). Lessons Learned Palliative patients frequently call 911 for help during acute crisis events and many of these patients do not wish conveyance to ED. The introduction of the ASTaR palliative clinical pathway provided safety netting and referral to appropriate care teams.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.361 | 0.057 |
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".