Impact of traumatic upper-extremity amputation on the outcome of injury caused by an antipersonnel improvised explosive device
Bibliographic record
Abstract
Background: We have previously reported a higher than expected rate of upper-extremity amputation (UEA) in victims of an antipersonnel improvised explosive device (AP-IED) compared with a similar cohort injured by antipersonnel mines (APM). The goal of this study was to describe the rate, severity and impact of UAE caused by an AP-IED. Methods: We analyzed a prospective database of 100 consecutive dismounted AP-IED victims with pattern 1 injuries to compare the outcomes of the cohort with UEA to that without. Results: We found that UEA (8 above elbow, 19 below elbow, 1 through elbow, 3 hand, 15 digit(s)) was much more prevalent with AP-IED than with APM (40% v. 6%, p < 0.001). In addition, UEA was associated with a higher rate of multiple amputations (39 [98%] v. 32 [53%], p < 0.001), bilateral lower-extremity amputation (LEA; 33 [82.5%] v. 30 [53.3%], p = 0.003) and facial injury (8 [20%] v. 4 [6.4%], p = 0.044), but not with pelvic disruption (10 [25%]), genitoperineal mutilation (19 [48%]), eye injury (6 [15%]), or skull fracture (6 [15%]). The fatality rate was higher in patients with UEA than in those without (12 [30%] v. 7 [12%], p = 0.022). Conclusion: Upper-extremity amputation is more prevalent with AP-IED than APM. Presence of UEA is associated with more severe injury and increased risk of death in AP-IED victims. Upper-limb injury has significant consequences for rehabilitation from LEA, which universally accompanies UEA in AP-IED victims. Upper-extremity injury should be amenable to prevention by innovative personal protective equipment designed to protect the flexed elbow.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".