Randomised controlled trial comparing marksmanship following application of a tourniquet or haemostatic clamp in healthy volunteers
Bibliographic record
Abstract
BACKGROUND: In a care under fire situation, a first line response to haemorrhage is to apply a tourniquet and return fire. However, there is little understanding of how tourniquets and other haemorrhage control devices impact marksmanship. METHODS: We compared the impact of the iTClamp and the Combat Application Tourniquet (CAT) on marksmanship. Following randomisation (iTClamp or CAT), trained marksmen fired an AR15 at a scaled silhouette target in prone unsupported position (shooting task). Subjects then attempted to complete the shooting task at 5, 10, 15, 30 and 60 min post-haemorrhage control device application. RESULTS: All of the clamp groups (n=7) completed the 60 min shooting task. Five CAT groups (n=6) completed the 5 min shooting task and one completed the 5 and 10 min shooting task before withdrawing. Four CAT groups were stopped due to unsafe handling; two stopped due to pain. When examining hits on mass (HOM) for the entire shooting task, there was no significant difference between tourniquet and iTClamp HOM at 5 min (p=0.18). However, there was a significant difference at 10 min, p=0.003 with tourniquet having significantly fewer HOM (1.7±2.7 HOM) than the iTClamp (8.1±3.3 HOM) group. The total effective HOM for the entire 60 min shooting task showed that the iTClamp group achieved significantly (p=0.001) more HOM than the tourniquet group. Over the entire 60 min shooting exercise, the iTClamp group achieved a median 72% (52/72) of available HOM while the tourniquet group obtained 19% (14/72). CONCLUSIONS: Application of a tourniquet to the dominant arm negates effective return of fire in a care under fire setting after a brief time window. Haemorrhage control devices that preserve function may have a role in care under fire situations, as preserving effectiveness in returning fire has obvious operational merits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".