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Reading with low vision: the impact of research on clinical management*

2011· review· en· W1746398764 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueClinical and Experimental Optometry · 2011
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)OptometryLow visionMagnificationNear visionMedicineVisual acuityOphthalmologyArtificial intelligenceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

AbstractThe past 40-years has seen a great expansion in low‐vision research, which has changed low‐vision teaching and our clinical management of people with low vision. Australian optometrists have contributed significantly to this research and the development of multidisciplinary low‐vision services. This paper reviews the research that has shaped our clinical assessment and patient management for reading by adults with low vision. The major improvements in clinical assessment of low vision for reading were brought about by the improvements in distance and near visual acuity measurements during the 1970s and research during the 1980s and 1990s showing the factors affecting the reading rate. These changes, together with a different method for representing the magnification provided by optical and electronic systems, allows a scientific, logical and practical method for prescribing magnification. An illustration of the step‐by‐step approach for prescribing magnification for low‐vision reading that is easy to apply in any clinical practice is included.Key words: age‐related macular degenerationlow visionreadingvisual acuity This article is part of the following collections: Women Research Pioneers in Australian Optometry Abbreviated versions of this paper were presented at both Queensland Vision 2007, the annual conference of Optometrists Association Australia, Queensland and Northern Territory Division (Noel Verney Memorial Lecture) and Vision 2008, the 9th International Conference on Low Vision, Montreal, Canada.Abbreviated versions of this paper were presented at both Queensland Vision 2007, the annual conference of Optometrists Association Australia, Queensland and Northern Territory Division (Noel Verney Memorial Lecture) and Vision 2008, the 9th International Conference on Low Vision, Montreal, Canada.NotesAbbreviated versions of this paper were presented at both Queensland Vision 2007, the annual conference of Optometrists Association Australia, Queensland and Northern Territory Division (Noel Verney Memorial Lecture) and Vision 2008, the 9th International Conference on Low Vision, Montreal, Canada.a. The M print size notation refers to the distance in metres, at which the overall size of the lower case letters subtends a visual angle of five minutes of arc.b. Point measurement refers to the overall dimension of the type body from the top of the ascending letters to the bottom of the descending letters, with the lower case letters being half the point size; 1 point = 1/72 inch (0.35 mm). Point sizes are commonly preceded by N, for example, N8 (8 point print), which was first used by LawCitation24 simply to indicate 'near'.c. A standard word is six characters.d. If the object is held at the focal length of a simple plus lens, only field of view varies with eye–lens distance. The largest field of view is given when the lens is in the spectacle plane.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
grokno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
opusno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.494
GPT teacher head0.731
Teacher spread0.237 · 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