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Record W2314873814 · doi:10.3109/01658107.2015.1134585

Current Perspectives of Bioptic Driving in Low Vision

2016· review· en· W2314873814 on OpenAlexaboutno aff
Robert Chun, Maria Cucuras, Walter M. Jay

Bibliographic record

VenueNeuro-Ophthalmology · 2016
Typereview
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLow visionIndependence (probability theory)SpectacleOptometryDepression (economics)Impaired VisionVisual DisturbancePsychologyMedicinePolitical scienceSurgeryLaw

Abstract

fetched live from OpenAlex

In this review, the authors discuss the current perspectives of spectacle-mounted telescopes (bioptics) used for driving among patients with vision impairments. The history, design, driving laws surrounding bioptic use, and developing programs in The Netherlands and Canada are discussed. Patients who have certain visual requirements and stable disease status may be eligible candidates to consider using a bioptic aid for driving. Given the high prevalence of depression among visually impaired patients, low vision specialists can work with neuro-ophthalmologists to maximise the independence and visual function of patients who have permanent vision impairments but capable of maintaining driving privileges.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.082
GPT teacher head0.478
Teacher spread0.396 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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