Resolution of Diplopia in Late Repair of Enophthalmos Following Facial Trauma
Bibliographic record
Abstract
Posttraumatic enophthalmos due to isolated or complex orbital fractures can contribute to diplopia. Current evidence recommends early repair. However, little is known about the outcome of enophthalmos correction when repair occurs beyond 30 days after trauma. In this systematic review, the authors aim to evaluate the current evidence on functional outcomes after delayed repair of posttraumatic enophthalmos.Two independent assessors undertook a systematic review of the literature using multiple databases. The authors' inclusion criteria identified studies involving patients at least 14 years of age who had surgical correction of persistent enophthalmos 30 days after initial trauma. Each eligible paper was included after critical appraisal using validated guidelines. Data on preoperative and postoperative enophthalmos and diplopia in each study was extracted. The pattern of fracture was also noted.The authors' search for the medical databases yielded 1053 articles, of which 6 eligible papers were included. Meta-analysis was performed. In patients with complex injuries involving orbital and mid-facial fractures, diplopia resolution was calculated to be 53%, and enophthalmos was corrected in 83% of the patients. In patients with isolated orbital fractures, 53% had resolution of their diplopia, and enophthalmos was corrected in 88% of the patients.Enophthalmos can be corrected to within 2 mm of the contralateral eye in both the isolated and complex orbital fractures in patients who present 30 days or greater after injury. Based on the studies reviewed, there is less predictability in diplopia resolution.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".