Civilian Airway Trauma: A Single‐Institution Experience
Bibliographic record
Abstract
BACKGROUND: Injuries to the airway in the neck and thorax are uncommon, but may be potentially life threatening. The objective of this study is to determine the clinical characteristics and outcomes for patients with airway injury. METHODS: From 1974 to 2014, a prospectively entered trauma database at a Level 1 trauma center was accessed to identify patients with injuries to the larynx, cervical trachea, or thoracic airway. Hospital charts were reviewed to obtain data on demographics, presentation, injury management, in-hospital and long-term morbidity and in-hospital mortality. Multivariate logistic regression was used to estimate predictors of mortality and long-term vocal cord morbidity. Data are expressed as N (%). RESULTS: One hundred and twenty patients were included (median injury severity score: 19 [interquartile range: 10-27]). There were 65 (54 %) blunt and 55 (46 %) penetrating injuries, with 90 (75 %) suffering multiple injuries. Sixteen (13 %) patients died from associated injuries (7: in ER; 9: after admission). Injuries were located in the cervical airway [101 (84 %)], thoracic airway [21 (18 %)], or both [2 (2 %)]. Eighty-six (72 %) patients were managed surgically. Predictors of in-hospital mortality included hemodynamic instability (OR 6.54, 95 % CI 1.11-37.14), GCS < 8 upon presentation (OR 4.35, 95 % CI 3.24-5.41), and head trauma (OR 4.10, 95 % CI 1.91-6.30). Fracture of cricoid or thyroid cartilages was a strong predictor of long-term vocal cord injury (OR 3.93, 95 % CI 1.25-12.59). CONCLUSIONS: Airway trauma remains a major challenge for early diagnosis, airway control, and management of both acute life-threatening injury and long-term morbidity.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".