Letter in Response to Kim M, Torrie I, Poisson R, Withers N, Bjarnason S, DaLuz LT, Pannell D, Beckett A, Tien HC. The Value of Live Tissue Training for Combat Casualty Care: A Survey of Canadian Combat Medics with Battlefield Experience in Afghanistan. Mil Med. 2017 Sep;182(9):e1834–e1840
Bibliographic record
Abstract
Letter in response to: Kim M, Torrie I, Poisson R, Withers N, Bjarnason S, DaLuz LT, Pannell D, Beckett A, Tien HC. The value of live tissue training for combat casualty care: A survey of Canadian combat medics with battlefield experience in Afghanistan. Mil Med. 2017 Sep;182(9):e1834–e1840 We thank Kim and colleagues for their study of the perceived value of live tissue training (LTT) on animals versus training on human patient simulators (HPS) in preparing combat medics for battlefield trauma care. However, their recommendation to continue support for LTT in military medical training is based on outdated literature, a study sample size that is too small to be credible, and a disproportionate reliance solely on medics’ emotive preference for a certain training modality without a critical assessment of how such training affects clinical outcomes. This is despite an abundance of peer-reviewed evidence from military studies that clearly demonstrates that HPS training methods teach trauma care skills as well as better than LTT while also being more cost-effective. Based on fiscal, logistical, and pedagogical benefits offered by human simulation technology, in 2017, the U.S. Coast Guard replaced LTT with HPS training methods, and as Kim and colleagues note that nearly 80% of NATO nations do not use animals for their military trauma training courses.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".