Re-evaluating the treatment of acute optic neuritis
Bibliographic record
Abstract
Clinical case reports and prospective trials have demonstrated a reproducible benefit of hypothalamic-pituitary-adrenal (HPA) axis modulation on the rate of recovery from acute inflammatory central nervous system (CNS) demyelination. As a result, corticosteroid preparations and adrenocorticotrophic hormones are the current mainstays of therapy for the treatment of acute optic neuritis (AON) and acute demyelination in multiple sclerosis.Despite facilitating the pace of recovery, HPA axis modulation and corticosteroids have failed to demonstrate long-term benefit on functional recovery. After AON, patients frequently report visual problems, motion perception difficulties and abnormal depth perception despite 'normal' (20/20) vision. In light of this disparity, the efficacy of these and other therapies for acute demyelination require re-evaluation using modern, high-precision paraclinical tools capable of monitoring tissue injury.In no arena is this more amenable than AON, where a new array of tools in retinal imaging and electrophysiology has advanced our ability to measure the anatomic and functional consequences of optic nerve injury. As a result, AON provides a unique clinical model for evaluating the treatment response of the derivative elements of acute inflammatory CNS injury: demyelination, axonal injury and neuronal degeneration.In this article, we examine current thinking on the mechanisms of immune injury in AON, discuss novel technologies for the assessment of optic nerve structure and function, and assess current and future treatment modalities. The primary aim is to develop a framework for rigorously evaluating interventions in AON and to assess their ability to preserve tissue architecture, re-establish normal physiology and restore optimal neurological function.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".