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Record W2988223940 · doi:10.22608/apo.2018299

Neuroprotection in Optic Neuropathy

2018· review· en· W2988223940 on OpenAlexaff
Leonard A. Levin

Bibliographic record

VenueAsia-Pacific Journal of Ophthalmology · 2018
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuroprotectionOptic nerveOptic neuropathyMedicineGlaucomaNeuroscienceRetinal ganglion cellDiseaseOphthalmologyPathologyBiologyPharmacology

Abstract

fetched live from OpenAlex

Almost all optic neuropathies are untreatable, motivating the search for new therapies that address the final common pathway of optic nerve disease, retinal ganglion cell loss. These neuroprotective strategies have been primarily studied in glaucoma, the most common optic neuropathy, but increasing also tested at the laboratory and animal model level in nonglaucomatous optic neuropathies. More recently, several clinical trials, most of which are randomized, have begun to examine whether neuroprotection is efficacious in human optic nerve disease. Many of these trials are reviewed, along with the critical issues in the major areas of optic neuropathy, particularly the site of injury, the mechanism of axonal damage, and disease-specific features relevant to neuroprotection studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.041
GPT teacher head0.336
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2018
Admission routes1
Has abstractyes

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