The efficiency of the medical priority dispatch system in improving patient outcomes
Bibliographic record
Abstract
Background: One of the essential aspects of acquiring favorable patients outcomes is to deliver appropriate care to them. In pre-hospital settings, the procedure begins with the dispatch since dispatchers manage the assistance requests. The medical priority dispatch system (MPDS) has been developed to improve the dispatchers performance. It follows algorithms and questions which aid in classifying situations based on callers answers to specific questions. This study aimed to assess the effectiveness of MPDS in enhancing patient outcomes. Methods: We searched PubMed, MEDLINE, Scopus, and six other electronic databases up to 17 August 2019. A combination of keywords relevant to MPDS was used to search for English published randomized controlled trials, case-control, and cohort studies evaluating MPDS and its impact on patient outcomes. Results: A total of 15 studies out of 755 were selected. All were observational cohort studies involving 1,394,366 participants; seven studies reported response time, four reported mortality rate, and four reported survival rates. We rated 14 of them as fair quality, and the rest were of poor quality based on the Newcastle-Ottawa scale. Eight studies supported the desired outcomes for the patient, whereas the rest depended on several factors to reach the desired outcomes. Conclusion: The majority of studies reported good results; however, there was no significant difference, and this might be an area, where the practice may change.
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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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".