Time-Domain and Spectral-Domain Optical Coherence Tomography of Retinal Nerve Fiber Layer in MS Patients and Healthy Controls
Bibliographic record
Abstract
Objective. The aim of this study was to compare retinal nerve fiber layer thickness (RNFLT) between spectral-domain (SD-) and time-domain optical coherence tomography (TD-OCT) in MS patients and healthy controls (HC). Furthermore, RNFLT between MS eyes with and without optic neuritis (ON) and HC should be explored. Finally, the relationship between RNFLT, disease duration, EDSS, and disease modifying therapy (DMT) should be established. Design. Prospective, cross-sectional study. Participants. 28 MS patients and 35 HC. Methods. Both groups underwent TD- and SD-OCT measurements. RFNLT was correlated between the two machines and between MS eyes with and without ON and HC. Furthermore, RNFLT was correlated to disease duration, EDSS and DMT. Results. A strong correlation (Pearson's r = 0.921, P < 0.001), but a statistically significant difference of 2 μm (P < 0.001), was found between the two devices. RNFLT was significantly different between MS eyes with history of ON (mean RFNLT (SD) 72.21 μm (15.83 μm)), MS eyes without history of ON 93.03 μm (14.25 μm), and HC 99.07 μm (7.23 μm) (P < 0.001). Conclusions. The measurements between different generation of OCT machines are not interchangeable, which should be taken into account if comparing results between different machines and switching OCT machine in longitudinal studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".