Quantifying axonal loss after optic neuritis with optical coherence tomography
Bibliographic record
Abstract
OBJECTIVE: To determine to what degree changes in retinal nerve fiber layer (RNFL) thickness after optic neuritis (ON) correlate with either visual recovery or impairment. METHODS: ON can cause visible defects within the RNFL, which can be quantified using optical coherence tomography (OCT). It may be possible to predict visual recovery by measuring RNFL loss after ON. Fifty-four patients underwent repeated evaluations with optical coherence tomography and standardized ophthalmic testing after ON. Regression analyses were used to determine the relationship between RNFL thickness and visual function. RESULTS: Thinning of the RNFL was seen in the majority of patients (74%), and it tended to occur within 3 to 6 months of ON. The average RNFL value was thinner (p<0.0001) in the affected (78 microm) compared with the unaffected eye (100 microm). Patients with incomplete visual recovery demonstrated greater RNFL loss after ON. Regression analyses demonstrated a threshold of RNFL thickness (75 microm), below which RNFL measurements predicted persistent visual dysfunction. INTERPRETATION: Determination of RNFL thickness may predict visual recovery after ON, and lower RNFL values correlate with impaired visual function. Optical coherence tomography may have a potential role as a surrogate marker for axonal integrity within the optic nerve among patients with ON.
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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 | 0.003 |
| 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 | 0.000 |
| 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 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".