Neuroprotection in neurodegenerations of the brain and eye: Lessons from the past and directions for the future
Bibliographic record
Abstract
Background: Neurological and ophthalmological neurodegenerative diseases in large part share underlying biology and pathophysiology. Despite extensive preclinical research on neuroprotection that in many cases bridges and unifies both fields, only a handful of neuroprotective therapies have succeeded clinically in either. Main body: Understanding the commonalities among brain and neuroretinal neurodegenerations can help develop innovative ways to improve translational success in neuroprotection research and emerging therapies. To do this, analysis of why translational research in neuroprotection fails necessitates addressing roadblocks at basic research and clinical trial levels. These include optimizing translational approaches with respect to biomarkers, therapeutic targets, treatments, animal models, and regulatory pathways. Conclusion: The common features of neurological and ophthalmological neurodegenerations are useful for outlining a path forward that should increase the likelihood of translational success in neuroprotective therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".