Diagnoses and factors associated with medical evacuation and return to duty among nonmilitary personnel participating in military operations in Iraq and Afghanistan
Bibliographic record
Abstract
BACKGROUND: Nonmilitary personnel play an increasingly critical role in modern wars. Stark differences exist between the demographic characteristics, training and missions of military and nonmilitary members. We examined the differences in types of injury and rates of returning to duty among nonmilitary and military personnel participating in military operations in Iraq and Afghanistan. METHODS: We collected data for nonmilitary personnel medically evacuated from military operations in Iraq and Afghanistan between 2004 and 2007. We compared injury categories and return-to-duty rates in this group with previously published data for military personnel and identified factors associated with return to duty. RESULTS: Of the 2155 medically evacuated nonmilitary personnel, 74.7% did not return to duty. War-related injuries in this group accounted for 25.6% of the evacuations, the most common causes being combat-related injuries (55.4%) and musculoskeletal/spinal injuries (22.9%). Among individuals with non-war-related injuries, musculoskeletal injuries accounted for 17.8% of evacuations. Diagnoses associated with the highest return-to-duty rates in the group of nonmilitary personnel were psychiatric diagnoses (15.6%) among those with war-related injuries and noncardiac chest or abdominal pain (44.0%) among those with non-war-related injuries. Compared with military personnel, nonmilitary personnel with war-related injuries were less likely to return to duty (4.4% v. 5.9%, p = 0.001) but more likely to return to duty after non-war-related injuries (32.5% v. 30.7%, p = 0.001). INTERPRETATION: Compared with military personnel, nonmilitary personnel were more likely to be evacuated with non-war-related injuries but more likely to return to duty after such injuries. For evacuations because of war-related injuries, this trend was reversed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".