Evaluating infra-orbital nerve injury in zygomaticomaxillary complex fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
Aim: This systematic review aimed to assess infraorbital nerve (ION) injury in zygomatico-maxillary complex (ZMC) fractures, specifically examining the incidence, diagnostic approaches, and comparative efficacy of surgical vs. conservative management. Methods: This review was completed in accordance with PRISMA 2020 principles and was recorded in PROSPERO (CRD42024545221). The Cochrane CENTRAL, Embase, MEDLINE, and PubMed databases were thoroughly examined between January 2015 and April 2025. Adult patients (≥18 years) with radiologically confirmed ZMC fractures who specifically reported infraorbital nerve outcomes, such as sensory abnormalities, diagnostic evaluations, and recovery after therapy, were eligible for inclusion in the studies. Cochrane RoB 2.0 for randomized trials and the modified Newcastle-Ottawa Scale for observational studies were used to assess the quality of the studies. RevMan 5.4 was used to carry out statistical pooling using a random-effects model. Results: Initially, 457 articles were found in the literature search; duplicates were eliminated, titles and abstracts were screened, and 48 complete texts were examined; 13 studies (1,308 patients) met the inclusion criteria. The meta-analysis includes 11 trials with 802 patients. After ZMC fractures, the combined incidence of ION damage was 51.9% (95% CI: 48%-55%). When compared to conservative therapies, early surgical decompression (within two weeks after the damage) consistently produced superior sensory recovery. With little use of objective testing (blink reflex, current perception thresholds), diagnostic evaluations were mostly based on subjective measurements (two-point discrimination, pain thresholds). Conclusion: Infraorbital nerve injury remains a prevalent complication in ZMC fractures, affecting approximately half of the patients. Surgical decompression performed early after injury significantly improves sensory recovery outcomes over conservative management. There remains substantial variability in diagnostic methods and outcome measurements, highlighting the need for standardized approaches. Future randomized controlled trials with robust methodologies and consistent outcome assessments are necessary to enhance clinical guidance and patient outcomes. Systematic Review Registration: : ROSPERO CRD42024545221.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".