Prognostic determination using optical coherence tomography compared with visual functions in optic neuritis
Bibliographic record
Abstract
Background: The majority of optic neuritis patients often notice improvement and gain stability of their visual functions, however, evidences of ongoing retinal nerve fiber layer (RNFL) thinning have been reported. Purposes: To investigate the correlation between RNFL thickness measured with Optical coherence tomography (OCT) and visual function tests and to determine the utility of OCT in visual prognostic assessment of optic neuritis. Method: A prospective study was performed in 12 patients with acute isolated optic neuritis. Best corrected visual acuity (BCVA), Swedish interactive threshold algorithms (SITA) 30-2 strategy on Humphrey field analyzer, and fast RNFL thickness analysis were performed on both affected and fellow eyes at baseline, 1.5, three and six months. Results: Mean BCVA and average mean deviation (MD) of the affected eye were significantly different from the fellow eyes at baseline. Affected eyes had significant thinner of RNFL at baseline, 1.5, three, and six months. Significant correlations between (i) mean RNFL thickness and BCVA at 1.5 ( r = 0.707, p = .010), (ii) mean RNFL thickness and MD at 1.5 months ( r = 0.674, p = .016) and six months( r = 0.710, p = .032), (iii) mean RNFL thickness at 1.5 months and MD at six months ( r = 0.782, p = .013). Conclusion: A correlation between RNFL thickness and visual function tests indicates that OCT might have roles in detection and prediction of RNFL damage in Optic neuritis (ON) patients despite no evidence of MS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".