Acute nontraumatic general surgical conditions on a combat deployment
Bibliographic record
Abstract
BACKGROUND: Literature is lacking on acute surgical problems that may be encountered on military deployment; even less has been written on whether or not any of these surgical problems could have been avoided with more focused predeployment screening. We sought to determine the burden of illness attributable to acute nontraumatic general surgical problems while on deployment and to identify areas where more rigorous predeployment screening could be implemented to decrease surgical resource use for nontraumatic problems. METHODS: We studied all Canadian Armed Forces (CAF) members deployed to Afghanistan between Feb. 7, 2006, and June 30, 2011, who required treatment for a nontraumatic general surgical condition. RESULTS: During the study period 28 990 CAF personnel deployed to Afghanistan; 373 (1.28%) were repatriated because of disease and 100 (0.34%) developed an acute general surgical condition. Among those who developed an acute surgical illness, 42 were combat personnel (42%) and 58 were support personnel (58%). Urologic diagnoses (n = 34) were the most frequent acute surgical conditions, followed by acute appendicitis (n = 18) and hernias (n = 12). We identified 5 areas where intensified predeployment screening could have potentially decreased the incidence of in-theatre acute surgical illness. CONCLUSION: Our findings suggest that there is a significant acute care surgery element encountered on combat deployment, and surgeons tasked with caring for this population should be prepared to treat these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".