Higher Complication Rates in Self‐Inflicted Gunshot Wounds After Microvascular Free Tissue Transfer
Bibliographic record
Abstract
Objectives/Hypothesis Microvascular free tissue transfer is often employed to reconstruct significant facial defects from ballistic injuries. Herein, we present our comparison of complications between self‐inflicted and non–self‐inflicted gunshot wounds after microvascular free tissue transfer. Study Design Retrospective case review. Methods Approval was obtained from the JPS institutional review board. We performed a retrospective review of cases of ballistic facial injuries between October 1997 and September 2017 that underwent vascularized free tissue transfer for reconstruction. Comparisons were made between self‐inflicted and non–self‐inflicted gunshot wounds after microvascular free tissue transfer. The χ2 test was used for all comparisons. P value and 95% confidence interval (CI) were reported. Results There were 73 patients requiring free flap reconstruction after gunshot wounds to the face during the study period. There was a statistically significant difference in the rates of nonunion between self‐inflicted and non–self‐inflicted wounds (P = .02, 95% CI: 0.9 to 35.8) There were also no significant differences in flap failure (P = .10, 95% CI: −2.8 to 24.2), plate exposure (P = .28, 95% CI: −6.7 to 33.0), wound infection (P = .40, 95% CI: −8.9 to 31.2), scar contracture (P = .60, 95% CI: −8.1 to 25.1), and fistula formation (P = .13, 95% CI: −2.8 to 28.8) between patients with self‐inflicted and those with non–self‐inflicted wounds. Overall, complication rates were significantly higher in the self‐inflicted group compared to the non–self‐inflicted group (P < .0001, 95% CI: 32.6 to 68.6). Conclusions Patients with self‐inflicted injuries had more complications postoperatively than those with non–self‐inflicted injuries. This is likely helpful in surgical planning and patient counseling. Level of Evidence 4 Laryngoscope, 129:837–840, 2019
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".