Post-operative Scar Comparison With Supraorbital Eyebrow and Upper Blepharoplasty Approach in the Management of Zygomaticomaxillary Complex Fractures
Bibliographic record
Abstract
Study Design A prospective randomized comparative study was conducted to evaluate the postsurgical scar with Supraorbital Eyebrow (SE) Approach and Upper Blepharoplasty (UB) Approach used for open reduction and internal fixation (ORIF) of zygomaticomaxillary complex (ZMC) fractures. Objective To evaluate and compare the post-operative scar using Vancouver Scar Scale (VSS) following ORIF of ZMC fractures with SE and UB approaches. Methods In this study, 88 patients with ZMC fractures requiring ORIF and meeting the inclusion criteria were recruited between 2019 and 2020. Patients were randomly divided into SE and UB group, 44 patients in each. Clinical and radiological assessment was done preoperatively and post-operative scar evaluation was carried out at different intervals over a period of 6 months using VSS. A blinded observer rated the scar. Results The results showed that after 6 months of surgery, all the 44 (100%) patients in UB group had a mild scar (VSS score 1–5), while in the SE group 34 (77.3%) patients had a mild scar (VSS score 1–5) and 10 (22.7%) had a moderate scar (VSS score 6–10). The difference between the 2 groups was statistically significant ( P-value = .001). Conclusions The UB approach has been established to be superior to SE approach in terms of post-operative scar as the results were statistically significant. This study can be used to advocate more frequent use of UB approach as compared to the previously popular SE approach for the management of ZMC fractures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".