Interventional studies performed in emergency medical communication centres: systematic review
Bibliographic record
Abstract
We aimed to both quantitatively and qualitatively describe interventional research performed in emergency medical communication centres. We conducted a systematic review of articles published in MEDLINE, Cochrane Central Register of Controlled Trials and Web of Science. Studies evaluating therapeutic or organizational interventions directed by call centres in the context of emergencies were included. Studies of call centre management for general practice or nonhealthcare agencies were excluded. We assessed general characteristics and methodological information for each study. Quality was evaluated by the Cochrane Risk of Bias tool or the Newcastle-Ottawa Scale. Among 3896 articles screened, we retained 59; 41 studies were randomized controlled trials (69%) and 18 (31%) were before-after studies; 41 (69%) took place in a single centre. For 33 (56%), 22 (37%) and 4 (7%) studies, the models used were simulation training, patient-based or experimental, respectively. The main topic was cardiac arrests (n = 45, 76%), with outcome measures of cardiopulmonary resuscitation quality and dispatch assistance. Among randomized controlled trials, risk of bias was unclear or high for selective reporting for 37 (90%) studies, low for blinding of outcomes for 34 (83%) and low for incomplete outcomes for 31 (76%). Regarding before-after studies, quality was high in 9 (50%) studies. Few interventional studies have been performed in call centres. Studies mainly involved simulation and focussed on cardiac arrest. The quality of studies needs improvement to allow for a better recognition and understanding of emergency medical call control.
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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 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".