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Record W3112356895 · doi:10.1126/scitranslmed.aaz7423

Bilateral visual improvement with unilateral gene therapy injection for Leber hereditary optic neuropathy

2020· article· en· W3112356895 on OpenAlexaff
Patrick Yu‐Wai‐Man, Nancy J. Newman, Valério Carelli, Mark L. Moster, Valérie Biousse, Alfredo A. Sadun, Thomas Klopstock, Catherine Vignal, Robert C. Sergott, Günther Rudolph, Chiara La Morgia, Rustum Karanjia, Magali Taiel, Laure Blouin, Pierre Burguière, Gerard Smits, Caroline Chevalier, Harvey Masonson, Yordak Salermo, Barrett Katz, Serge Picaud, David J. Calkins, José‐Alain Sahel

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsOttawa Hospital
FundersGENSIGHT BIOLOGICS PARISMedical Research CouncilMinistero della SaluteE-RareAgence Nationale de la RechercheSight Research UKMoorfields Eye Hospital NHS Foundation TrustBundesministerium für Bildung und ForschungIsaac Newton TrustNational Institute for Health and Care Research
KeywordsGenetic enhancementOptic neuropathyMedicineLeber's hereditary optic neuropathyOphthalmologyOptic nerveGeneBiologyGenetics

Abstract

fetched live from OpenAlex

= 0.894). At week 96, 25 subjects (68%) had a clinically relevant recovery in BCVA from baseline in at least one eye, and 29 subjects (78%) had an improvement in vision in both eyes. A nonhuman primate study was conducted to investigate this bilateral improvement. Evidence of transfer of viral vector DNA from the injected eye to the anterior segment, retina, and optic nerve of the contralateral noninjected eye supports a plausible mechanistic explanation for the unexpected bilateral improvement in visual function after unilateral injection.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.267
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations226
Published2020
Admission routes1
Has abstractyes

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