Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis
Bibliographic record
Abstract
Objective: Optical coherence tomography (OCT) noninvasively quantifies retinal nerve fiber layer (RNFL) thickness. Studies show RNFL thinning in multiple sclerosis (MS), and we assessed its association with brain atrophy. Methods: RNFL thickness was measured in 40 patients with MS and 15 controls. Brain parenchymal fraction (BPF) and partial brain volumes were estimated from cranial MRI scans using SIENA-X. Multiple linear regression modeling assessed the association between OCT and MRI measures of atrophy. Results: Minimum RNFL thickness and subject age together predict 21% (p = 0.005) of the variance in BPF in all patients with MS and 43% (p = 0.003) of the variance in BPF in the subgroup with relapsing remitting MS (RRMS; n = 20). The partial correlation coefficient between BPF and minimum RNFL thickness, controlling for age, is 0.46 (p = 0.003) in all patients with MS and 0.69 (p = 0.001) in patients with RRMS. These associations are driven by CSF volume but not by gray or white matter volume. There is no significant association of these variables among controls. Conclusions: In multiple sclerosis (MS), retinal nerve fiber layer thickness is associated with brain parenchymal fraction and CSF volume. These data suggest that quantification of axonal thickness in the retina by optical coherence tomography (OCT) provides concurrent information about MRI brain abnormality in MS. OCT should be examined in longitudinal studies to determine if it could be used as an outcome measure in clinical trials of neuroprotective drugs. GLOSSARY: BPF = brain parenchymal fraction; EDSS = Expanded Disability Status Scale; KKI = Kennedy Krieger Institute; MNI = Montreal Neurological Institute; MPRAGE = magnetization-prepared rapid gradient echo; MS = multiple sclerosis; OCT = optical coherence tomography; PPMS = primary progressive MS; RNFL = retinal nerve fiber layer; RRMS = relapsing remitting MS; SPMS = secondary progressive MS; TMV = total macular volume.
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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 | 0.003 |
| 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) | 0.002 | 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".