Factors affecting aesthetic results in patients undergoing craniofacial reconstruction following maxillofacial trauma
Bibliographic record
Abstract
OBJECTIVES: To investigate the factors influencing the cosmetic outcomes and prognosis of patients undergoing maxillofacial trauma reconstruction. METHODS: A retrospective analysis was conducted on the clinical data of 335 patients who underwent maxillofacial trauma surgery criteria at Yunfu People's Hospital from March 2016 to June 2023. The Face-Q facial cosmetic rating scale was utilized to evaluate outcomes, with scores above 60 deemed the good prognosis group (n=234) and scores below 60 as the poor prognosis group (n=101). Two groups were compared in terms of demographic data, type of trauma, clinical presentation, intraoperative indicators, postoperative serum parameters and nutritional levels, Hamilton Anxiety Scale (HAS), Pittsburgh Sleep Quality Index (PSQI) sleep quality scores. Postoperative recovery and the incidence of complications were documented. Correlation analysis was performed, and Logistic regression analysis was used to determine influencing factors. RESULTS: Patients in the good prognosis group were significantly younger than those in the poor prognosis group (38.15 ± 10.32 vs. 46.69 ± 12.15, P < 0.001). Postoperative protein intake (65.81% vs. 33.66%, P < 0.001) and levels of anxiety (5.57 ± 1.52 vs. 6.61 ± 1.47, P < 0.001) were also better in the good prognosis group. There were significant differences in scar formation (5.57 ± 1.52 vs. 6.61 ± 1.47, P < 0.001), postoperative complications (2.56% vs. 8.91%, P=0.022) and scar hypertrophy (1.28% vs. 6.93%, P=0.015) between the two groups. Logistic regression analysis revealed that age (OR=1.07, 95% CI: 1.039-1.109), protein intake adequacy (OR=0.297, 95% CI: 0.141-0.625), HAS scores (OR=1.295, 95% CI: 1.011-1.658), infection (OR=11.579, 95% CI: 2.656-52.274), and Vancouver Scar Scale (VSS) score (OR=15.672, 95% CI: 7.379-33.285) were significantly associated with aesthetic outcomes. The ROC analysis showed that their combined prediction had an AUC of 0.920, indicating good predictive value. CONCLUSIONS: Younger age, adequate protein intake, lower anxiety scores, better scar assessment, and lower infection rates were associated with better prognosis. These findings emphasize the importance of addressing these factors to optimize outcome in craniofacial trauma reconstruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".