Preoperative Diplopia
Bibliographic record
Abstract
BACKGROUND: No consensus exists in the literature regarding the risk factors associated with new or residual diplopia after pure orbital blowout fracture (BOF) repair. OBJECTIVE: To assess and evaluate the risk factors associated with diplopia after surgical repair of pure BOF. METHODS: Patients with pure BOF who were managed surgically were identified in an 11-year period at the McGill University Health Center. The association between new or residual diplopia postsurgical repair and various risk factors was assessed using chi and Fisher exact tests, and multivariate analysis was conducted using logistic regression. RESULTS: A total of 61 patient charts were reviewed. Results demonstrated the presence of preoperative diplopia and radiologic evidence of extraocular muscle (EOM) swelling to be strongly associated with diplopia at 6 months after repair (P < 0.05). Patients who presented preoperatively with diplopia had a 9.91 times greater probability of developing diplopia postoperatively (P = 0.035; 95% confidence interval, 1.17-83.80). CONCLUSIONS: Preoperative diplopia is the best predictor of the presence of postoperative diplopia after BOF repair. Initial injury to the EOM leading to EOM swelling and preoperative diplopia seems to be the origin of diplopia after surgical repair of pure BOF.
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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 | 0.001 | 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) | 0.012 | 0.001 |
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".