Rounding of the Inferior Rectus Muscle as a Predictor of Enophthalmos in Orbital Floor Fractures
Bibliographic record
Abstract
In spite of established indications for early operative repair of orbital floor fractures 7-10% of patients treated nonoperatively develop enophthalmos. Clearly further indications for repair are required to prevent these post-injury complications. Rounding of the inferior rectus muscle on coronal computerized tomography (CT) scan results from a loss of soft tissue and bony support and may therefore be predictive of late enophthalmos.A four-year institutional review was conducted to identify patients with orbital floor fractures that had been treated nonoperatively. Patients were recruited for late clinical follow-up (mean 30 months) where clinically significant enophthalmos and diplopia were measured. Clinical results were correlated with measurements of the height-to-width ratio of the inferior rectus muscle on CT scans by a blinded examiner. Eighteen of 78 patients were available for late follow-up. Sixteen patients had no enophthalmos whereas 2 patients had enophthalmos. The inferior rectus height-to-width ratios measured in the unaffected orbits were statistically similar between the two groups. There was a significantly increased height-to-width ratio exceeding 1.00 in the affected orbit when the enophthalmos group was compared to the no enophthalmos group.A height-to-width ratio of the inferior rectus muscle on coronal CT scan of greater than or equal to 1.00 is predictive of late enophthalmos.
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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 | 0.003 |
| 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) | 0.001 | 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".