Palliative paramedicine: An interrupted time series analysis of pre-hospital guideline efficacy
Bibliographic record
Abstract
Background: Paramedics are increasingly involved in palliative care and often support community-based palliative care service delivery to facilitate integrated practice. However, the impact of specific palliative care guidelines on clinical practice remains unknown. Aim: To determine the impact of an ambulance service palliative care guideline on rates of supportive medication administration and non-transport. Design: A retrospective cohort study of electronic patient care records from January 2014 to June 2023. Baseline characteristics were compared pre- and post-guideline introduction. Interrupted time series analysis was performed to examine guideline efficacy. Setting/participants: Patients of all ages receiving palliative care who were attended by paramedics in Victoria, Australia. Results: A total of 31,579 patients were included. The median age was 75 years (IQR = 64–84 years), and 56.4% were men. Overall, 25.8% of patients were not transported to hospital. Following guideline introduction, there were no significant trend changes in administration of supportive medications. However, the non-transport rate increased significantly per month (0.2%, p = 0.007), amounting to a 9.9% ( p = 0.020) total increase by the end of the study period compared to a scenario in which the guideline had not been introduced. Subgroup analysis of patients diagnosed with ‘pain’ or attended after-hours also showed significant increases in non-transport (monthly increase: pain 0.3%, p = 0.003; after-hours 0.3%, p < 0.001; total increase: pain 29.7%, p < 0.001, after-hours 22.6%, p = 0.001). Conclusions: Introduction of a palliative care guideline was associated with a decrease in ambulance transport to emergency departments, allowing more patients continuity of care in the community.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".