Advanced or Basic Life Support for Trauma: Meta-analysis and Critical Review of the Literature
Bibliographic record
Abstract
BACKGROUND: The question of whether to use advanced life support (ALS) or basic life support (BLS) for trauma patients in the prehospital setting has been much debated and still lacks a clear answer. The purpose of this study was to conduct a comprehensive critical review of the literature regarding this controversy METHODS: A total of 174 articles on prehospital ALS or BLS for trauma were reviewed. Fifteen of these studies were found to involve mortality statistics for both ALS- and BLS-treated patients. Odds ratios were calculated for survival in ALS versus BLS and summarized across studies on the basis of multivariate scoring systems that incorporated both design and methodological assessment. Overall odds ratios for all studies were calculated on the basis of both raw data from the papers, and weighted odds ratios were calculated from the scoring systems. RESULTS: Six studies were scored as being methodologically average (5 favoring BLS and 1 favoring ALS), two were scored as good (1 favoring BLS and 1 favoring ALS), seven as excellent (6 favoring BLS and 1 favoring ALS). Ten studies had an average study design score (6 favoring BLS and 4 favoring ALS) and seven had a good study design score (6 favoring BLS and 1 favoring ALS). Weighted odds ratio for dying was 2.59 for patients receiving ALS compared with those receiving BLS. The crude odds ratio was 2.92. CONCLUSION: The aggregated data in the literature have failed to demonstrate a benefit for on-site ALS provided to trauma patients and support the scoop and run approach.
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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 | 0.051 | 0.132 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".