Effect of special operational forces surgical resuscitation teams on combat casualty survival: A narrative review
Bibliographic record
Abstract
IMPORTANCE: The most common cause of preventable death on the conventional battlefield or on special operations force (SOF) missions is hemorrhage. SOF missions may take place in remote and austere locations. Many preventable deaths in combat occur within 30 min of wounding. Therefore, SOF damage control resuscitation (DCR) and damage control surgery (DCS) teams may improve combat casualty survival in the SOF environment. OBJECTIVE: To determine the effect of SOF DCR and DCS teams on combat casualty survival. Also, to describe commonalities in team structure, logistics, and blood product usage. DESIGN: A narrative review of the English literature used a Medline and Embase search strategy. The authors were contacted for more details as required. The risk of bias was assessed using the Cochrane Collaboration's ROBINS-I tool. Pooling of data was not done to the heterogeneity of studies. RESULTS: Weak evidence was identified showing a clinical benefit of SOF DCR and DCS teams. Conflicting evidence from less rigorous studies was also found. The overall risk of bias using ROBINS-I was serious to critical. Several commonalities in team structure, training, and logistics were found. CONCLUSIONS AND RELEVANCE: There is conflicting evidence regarding the effect SOF DCR and DCS teams have on combat casualty survival. There is no strong evidence that SOF DCR and DCS teams cause harm. More robust data collection is recommended to evaluate these teams.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".