Qualitative and Quantitative Magnetic Resonance Imaging in Bacterial Orbital Cellulitis
Bibliographic record
Abstract
Purpose To summarise the qualitative and quantitative parameters of bacterial orbital cellulitis (OC) on magnetic resonance imaging (MRI) and explore their clinical correlations.Methods Multi-centre retrospective study with inclusion of patients of all ages with OC who underwent MRI. Patients with isolated pre-septal cellulitis, bilateral disease and poor-quality scans were excluded. An enlargement ratio for extraocular muscles (EOMs) was calculated by dividing maximal EOM measurements from the affected side by the contralateral side.Results Twenty MRI scans from twenty patients (Mean age: 40.8 ± 24.3 years old, M: F = 15:5) between 2011 and 2022 were analysed. Three (15.0%) cases were paediatric patients (<18 years old). All cases had both pre-septal and orbital fat involvement. The EOM were affected in nineteen cases, with the superior muscle complex (18/19, 94.7%) most commonly affected. Mean enlargement ratio (1.30, Range: 1.04–1.82) was greatest for the medial rectus on axial views on T1 and fat-suppressed contrast-enhanced T1 (FS CE T1). Optic peri-neuritis was present in eleven (55.0%) patients, whilst two (9.5%) cases had optic neuritis. A greater degree of proptosis was observed in patients with optic neuropathy and those who underwent surgical intervention compared to those without (p = .002 and p = .002, respectively).Conclusion MRI remains an important imaging modality for evaluating complicated OC. However, qualitative features may lack accuracy and is not a reproducible means of analysis. Simple quantitative parameters, such as proptosis and EOM measurements, correlate with high-risk clinical features and may have utility in predicting clinical course.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".