Bibliographic record
Abstract
PURPOSE: To determine the clinical differences between sclerosing orbital inflammation (SOI) isolated to the orbit and SOI manifesting as part of systemic inflammatory disease. METHODS: For the case series, the authors identified patients with SOI from their patient database and tabulated their clinical data. For the literature review, a search for case reports of SOI in the English literature was performed. The clinical data of each were tabulated and analyzed. RESULTS: For the case series, 13 cases of SOI were identified, 2 with multifocal fibrosclerosis (MFF) and 4 with other systemic inflammatory diseases. Of the 5 bilateral cases, 4 (80%) had some form of systemic inflammatory disease including 2 with MFF. Only 2 (25%) of the 8 unilateral cases had systemic inflammatory disease. The average ESR was 7 mm/hour for those without systemic disease and 41 mm/hour with evidence of systemic disease (p = 0.038). For the literature review, the authors identified 68 case reports of SOI not previously reported in large case series, 34 associated with MFF, 5 associated with other systemic disease, and 29 not associated with any systemic disease. Sixty-nine percent of cases with systemic disease had bilateral orbital involvement, whereas only 3.6% of those without systemic disease were bilateral (p = 1.15 × 10(-8)). Twenty of the cases with MFF noted ESR (83 mm/hour, mean), 95% of which were above normal. ESR was noted in only one non-MFF, nonsystemic disease case and was normal. CONCLUSIONS: SOI in the setting of MFF or other systemic inflammatory disease is often bilateral and associated with an elevated ESR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".