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Record W3122247359 · doi:10.1016/j.optom.2020.10.004

Restitution of potential visual acuity in low vision patients with the use of yoke prisms

2021· article· en· W3122247359 on OpenAlexaff
Samuel N. Markowitz, Jack E. Teplitsky, Maryam Taheri-Shirazi

Bibliographic record

VenueJournal of Optometry · 2021
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsWestern UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVisual acuityMedicineYoke (aeronautics)RestitutionOphthalmologyOptometryComputer science

Abstract

fetched live from OpenAlex

To determine the efficacy of prisms when used for redirection of incoming images towards the preferred reinal loci (PRLs) for restitution of potential visual acuity (PVA) in low vision cases with age-related macular degeneration (AMD). Retrospective comparative interventional case series review. Low vision rehabilitation (LVR) protocol used included best corrected visual acuity (BCVA), PVA, topographic PRL identification and use of prisms to produce image redirection to the presumed PRL. The primary outcome measure selected for analysis was BCVA for viewing distance targets after use of yoke prisms. Image relocation with prisms in patients with AMD resulted in significantly better BCVA levels (t32 = 8.57, p < 0.0001) in the better eye. Distance BCVA levels achieved were almost identical to PVA levels (t32 = 0.415, p < 0.681) (y= -0.136 + 1.195x, r = 0.8333, p < 0.001). Use of yoke prisms for image redirection towards a peripheral identifiable PRL may result in PVA restitution in most cases.

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)0.003
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
Metaresearch0.0010.003
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)0.0010.000

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 head0.025
GPT teacher head0.383
Teacher spread0.359 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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