Medical lessons learnt from the US and Canadian experience of treating combat casualties from Afghanistan and Iraq
Bibliographic record
Abstract
The Winston Churchill Memorial Trust, established in 1965, funds Travelling Fellowships and both authors visited hospitals in Germany, Canada and the USA regarded as centres of excellence with expertise in the early care, reconstruction and rehabilitation of the combat casualties of our NATO Allies, as recipients of these Fellowships. This article presents some of the lessons learnt in the field of musculoskeletal trauma and rehabilitation from the Canadian and US military medical systems. In trauma, there were significant differences in wound debridement policy, use of external fixators for fractures, primary use of circular frames for open tibial fractures and a far more liberal use of bone morphogenetic protein in fracture treatment. Differences in soft tissue reconstruction policy regarding flaps for soft tissue cover over exposed bone, near-universal usage of topical negative pressure dressings and use of Allgöwer-Donati suture pattern to close all wounds were noted. Ertl amputation osteoplasty, a modified form of transtibial amputation, had also been reintroduced. In rehabilitation, the management of heterotopic ossification, in particular with imaging techniques and excision surgery, was identified. For the upper limb, we observed the patient training required to use a myoelectric hand and the future possibility of targeted muscle re-innervation to make controlling these myoelectric prostheses more natural using innate motor patterns. For the lower limb, we found we used identical above knee prostheses. For patients who have had limb reconstruction and have poor function, an energy-storing orthosis was demonstrated to compensate for the loss of range of motion and muscle power.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".