Benefits of targeted deployment of physician-led interprofessional pre-hospital teams on the care of critically Ill and injured patients: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Over the past three decades, more advanced pre-hospital systems have increasingly integrated physicians into targeted roles, forming interprofessional teams. These teams focus on providing early senior decision-making and advanced interventions while also ensuring rapid transport to hospitals based on individual patient needs. This paper aims to evaluate the benefits of an inter-professional care model compared to a model where care is delivered solely by paramedics. METHODOLOGY: A meta-analysis and systematic review were conducted using the guidelines of PRISMA 2020. Articles were identified through a systematic search of three databases and snowballing references. A systematic review was conducted of articles that met the inclusion criteria, and a suitable subset was included in a meta-analysis. The survival and mortality outcomes from the studies were then pooled using the statistical software Review Manager (RevMan) Version 8.2.0. RESULTS: Two thousand two hundred ninety-six articles were found from the online databases and 86 from other sources. However, only 23 articles met the inclusion criteria of our study. A pooled analysis of the outcomes reported in these studies indicated that the mortality risk was significantly reduced in patients who received pre-hospital care from interprofessional teams led by physicians compared with those who received care from paramedics alone (AOR 0.80; 95% CI [0.68, 0.91] p = 0.001). The survival rate of critically ill or injured patients who received pre-hospital care from interprofessional teams led by physicians was increased compared to those who received care from paramedics alone (AOR 1.49; 95% CI [1.31, 1.69] P < 0.00001). CONCLUSIONS: The results of our analysis indicate that the targeted deployment of interprofessional teams led by physicians in the pre-hospital care of critically ill or injured patients improves patient outcomes.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".