Retinal Nerve Fiber Layer and Future Risk of Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Optical coherence tomography (OCT)--measured retinal nerve fiber layer (RNFL) values may represent a surrogate biomarker for axonal integrity in multiple sclerosis (MS). The purpose of this study was to determine whether RNFL measurements obtained within two years of an optic neuritis (ON) event distinguish patients at increased risk of developing clinically-definite MS (CDMS). METHODS: Fifty consecutively sampled patients who experienced a single ON event were followed prospectively for a mean period of 34 months with OCT testing. Values of RNFL in clinically-affected and non-affected eyes were compared between patients who developed CDMS and those that did not develop MS after ON. FINDINGS: Twenty-one patients (42%) developed CDMS during the course of the study, with a mean conversion time of 27 months. Mean RNFL values were thinner in the clinically-affected eyes of non-MS patients than CDMS eyes after one year (p = 0.0462) due to more severe ON events in the former. By year two, CDMS patients manifested more recurrent ON events and RNFL thinning than non-MS patients. Temporal RNFL values were thinner in the non-affected eyes of CDMS patients with a trend towards significance (p = 0.1302). INTERPRETATION: Our results indicate that RNFL thickness does not reliably distinguish patients at higher risk of converting to CDMS after ON. The severity of ON has a greater effect on RNFL thickness than risk of CDMS at one year. The CDMS patients demonstrate progressive RNFL thinning likely due to recurrent sub-clinical ON events, which may help differentiate them from non-MS patients over time.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".