Bibliographic record
Abstract
Historically, head and neck injuries constituted 16 to 20% of all nonfatal combat injuries. However, advances in body and vehicle armor in the context of the use of ambushes and improvised explosive devices by enemy combatants have resulted in fewer fatalities from head and neck wounds, and thus the incidence of nonfatal head and neck injuries has risen to as high as 52%. Despite this increase, data regarding specific injury distributions, surgical cases, and approaches to repair are lacking in the current literature. We conducted a study to systematically review the current literature regarding head and neck injuries and reconstructions during Operation Iraqi Freedom and Operation Enduring Freedom-Afghanistan. We found 44 articles that met our inclusion criteria. These articles covered 17,461 head and neck wounds sustained by 12,105 patients. Superficial soft-tissue facial injuries were most common wounds (31.7% of cases), followed by wounds to the neck (25.2%) and midface (17.9%). The 44 articles listed 5,122 discrete surgical reports covering 5,758 procedures. Of these procedures, simple facial laceration repairs (25.2%) and ophthalmologic surgeries (12.1%) were the most common soft-tissue repairs, and mandibular reconstructions (11.3%) were the most common type of bony reconstruction. Major flap reconstructions for coverage were required in only 0.4% of procedures. This information will be valuable for educating those involved in otolaryngology training programs, as well as civilian otolaryngologists regarding the types of injury patterns they should expect to see and treat in the returning veteran population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".