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Record W3163673421 · doi:10.1016/j.ajo.2021.04.013

Assessing Photoreceptor Status in Retinal Dystrophies: From High-Resolution Imaging to Functional Vision

2021· article· en· W3163673421 on OpenAlexfundno aff
José‐Alain Sahel, Kate Grieve, Chloé Pagot, Colas Authié, Saddek Mohand‐Saïd, Michel Pâques, Isabelle Audo, Karine Thaís Becker, Anne-Elisabeth Chaumet-Riffaud, Line Azoulay, Emmanuel Gutman, Thierry Léveillard, Christina Zeitz, Serge Picaud, Deniz Dalkara, Katia Marazova

Bibliographic record

VenueAmerican Journal of Ophthalmology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersEuropean Research CouncilBiogenH2020 European Research CouncilAgence Nationale de la RechercheNovartisFoundation Fighting Blindness
KeywordsFovealOphthalmologyVisual acuityMedicineOptical coherence tomographyRetinaRetinalOpticsPhysics

Abstract

fetched live from OpenAlex

Purpose To describe the value of integrating phenotype/genotype data, disease staging, and evaluation of functional vision in patient-centered management of retinal dystrophies. Methods (1) Cross-sectional structure-function and retrospective longitudinal studies to assess the correlations between standard fundus autofluorescence (FAF), optical coherence tomography, visual acuity (VA), and perimetry (visual field [VF]) examinations to evaluate photoreceptor functional loss in a cohort of patients with rod-cone dystrophy (RCD); (2) flood-illumination adaptive optics (FIAO) imaging focusing on photoreceptor misalignment and orientation of outer segments; and (3) evaluation of the impact of visual impairment in daily life activities, based on functional (visual and mobility) vision assessment in a naturalistic environment in visually impaired subjects with RCD and subjects treated with Luxturna Ⓡ for RPE65 -related Leber congenital amaurosis before and after therapy. Results The results of the cross-sectional transversal study showed that (1) VA and macular sensitivity were weakly correlated with the structural variables; and (2) functional impairment (VF) was correlated with reduction of anatomical markers of photoreceptor structure and increased width of autofluorescent ring. The dimensions of the ring of increased FAF evolved faster. Other criteria that differed among groups were the lengths of the ellipsoid zone, the external limiting membrane, and the foveal thickness. FIAO revealed a variety of phenotypes: paradoxical visibility of foveal cones; heterogeneous brightness of cones; dim, inner segment–like, and RPE-like mosaic. Directional illumination by varying orientation of incident light (Stiles-Crawford effect) and the amount of side illumination (gaze-dependent imaging) affected photoreceptor visibility. Mobility assessment under different lighting conditions showed correlation with VF, VA, contrast sensitivity (CS), and dark adaptation, with different predictive values depending on mobility study paradigms and illumination level. At high illumination level (235 lux), VF was a predictor for all mobility performance models. Under low illumination (1 and 2 lux), VF was the most significant predictor of mobility performance variables, while CS best explained the number of collisions and segments. In subjects treated with Luxturna Ⓡ , a very favorable impact on travel speed and reduction in the number of collisions, especially at low luminance, was observable 6 months following injection, in both children and adults. Conclusions Our results suggest the benefit of development and implementation of quantitative and reproducible tools to evaluate the status of photoreceptors and the impact of both visual impairment and novel therapies in real-life conditions. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.284
Teacher spread0.274 · 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 designObservational
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

Citations42
Published2021
Admission routes1
Has abstractyes

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