Multiple trauma management in mountain environments - a scoping review
Bibliographic record
Abstract
BACKGROUND: Multiple trauma in mountain environments may be associated with increased morbidity and mortality compared to urban environments. OBJECTIVE: To provide evidence based guidance to assist rescuers in multiple trauma management in mountain environments. ELIGIBILITY CRITERIA: All articles published on or before September 30th 2019, in all languages, were included. Articles were searched with predefined search terms. SOURCES OF EVIDENCE: PubMed, Cochrane Database of Systematic Reviews and hand searching of relevant studies from the reference list of included articles. CHARTING METHODS: Evidence was searched according to clinically relevant topics and PICO questions. RESULTS: Two-hundred forty-seven articles met the inclusion criteria. Recommendations were developed and graded according to the evidence-grading system of the American College of Chest Physicians. The manuscript was initially written and discussed by the coauthors. Then it was presented to ICAR MedCom in draft and again in final form for discussion and internal peer review. Finally, in a face-to-face discussion within ICAR MedCom consensus was reached on October 11th 2019, at the ICAR fall meeting in Zakopane, Poland. CONCLUSIONS: Multiple trauma management in mountain environments can be demanding. Safety of the rescuers and the victim has priority. A crABCDE approach, with haemorrhage control first, is central, followed by basic first aid, splinting, immobilisation, analgesia, and insulation. Time for on-site medical treatment must be balanced against the need for rapid transfer to a trauma centre and should be as short as possible. Reduced on-scene times may be achieved with helicopter rescue. Advanced diagnostics (e.g. ultrasound) may be used and treatment continued during transport.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".