Retinal inner nuclear layer volume reflects inflammatory disease activity in multiple sclerosis; a longitudinal OCT study
Bibliographic record
Abstract
Background The association of peripapillary retinal nerve fibre layer (pRNFL) and ganglion cell-inner plexiform layer (GCIPL) thickness with neurodegeneration in multiple sclerosis (MS) is well established. The relationship of the adjoining inner nuclear layer (INL) with inflammatory disease activity is less well understood. Objective The objective of this paper is to investigate the relationship of INL volume changes with inflammatory disease activity in MS. Methods In this longitudinal, multi-centre study, optical coherence tomography (OCT) and clinical data (disability status, relapses and MS optic neuritis (MSON)) were collected in 785 patients with MS (68.3% female) and 92 healthy controls (63.4% female) from 11 MS centres between 2010 and 2017 and pooled retrospectively. Data on pRNFL, GCIPL and INL were obtained at each centre. Results There was a significant increase in INL volume in eyes with new MSON during the study ( N = 61/1562, β = 0.01 mm 3 , p < .001). Clinical relapses (other than MSON) were significantly associated with increased INL volume (β = 0.005, p = .025). INL volume was independent of disease progression (β = 0.002 mm 3 , p = .474). Conclusion Our data demonstrate that an increase in INL volume is associated with MSON and the occurrence of clinical relapses. Therefore, INL volume changes may be useful as an outcome marker for inflammatory disease activity in MSON and MS treatment trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".