ETDRS letter scoring method improves the accuracy of 1.25% low-contrast visual acuity measurement in optic neuritis secondary to MS
Bibliographic record
Abstract
Abstract Objectives To examine whether the dynamic range of 1.25% low-contrast visual acuity (LCVA) measurement in MS patients with optic neuritis (ON) can be improved by using the Early Treatment Diabetic Retinopathy Study (ETDRS) letter scoring method. Methods LCVA was tested using 2.5% and 1.25% low contrast ETDRS-style 4m Sloan letter charts. When ≥20 letters were read correctly, the letter score was equal to letter count plus 30. If <20 letters were read correctly, the letter score was equal to letter count at 4m plus the total number of letters read correctly at 1m. Results 51 relapsing-remitting MS patients with unilateral ON were enrolled and 60.8% ON eyes had a 1.25% LCVA letter count worse than 1 line (5 letters). In ON eyes with <20 letter count, ETDRS letter score showed a significantly improved correlation with both macular GCIPL thickness (r=0.71, p<0.0001; vs r=0.31, p=0.08 for letter count) and VEP latency (r=-0.47, p=0.003; vs r=-0.15, p=0.4 for letter count). Discussion Given ON with VEP monitoring has been frequently used as a model in MS remyelination clinical trials, our proposed ETDRS-style LCVA letter scoring method may be considered to enhance the functional outcome measure, which is necessary for regulatory approval.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".