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Record W1716734861 · doi:10.3233/wor-2011-1151

The multi-disciplinary nature of low vision rehabilitation~-- A case report

2011· article· en· W1716734861 on OpenAlexaff
Michelle Markowitz, Rachel E Markowitz, Samuel N. Markowitz

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsToronto Western HospitalTrillium Health CentreToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsRehabilitationSpecialtyMacular degenerationOptometryLow visionReferralDisciplineVisual rehabilitationReading (process)MedicineMedical educationPsychologyComputer scienceOphthalmologyVisual acuityNursingPhysical therapyPsychiatrySociology

Abstract

fetched live from OpenAlex

This paper presents the case of a 47-year-old female with low vision secondary to high myopic macular degeneration who remains active in the work force as a spiritual and religious care coordinator for a large institution. An ophthalmologist with a specialty in low vision rehabilitation initially assessed the client. The ophthalmologist prescribed optical devices which used residual retinal vision available at preferred retinal loci. This availed better vision for viewing targets located at far, near and intermediate distances from the client. An optician provided and dispensed the devices prescribed to the client. Additionally, the ophthalmologist made a referral to an occupational therapist. The occupational therapist conducted a series of sessions to further enhance reading and writing skills and a work place assessment aimed at optimizing workplace conditions in order to achieve optimal functional vision. This case illustrates and emphasizes the multi-disciplinary nature of low vision rehabilitation, which involved in this case co-operation between ophthalmology, occupational therapy and opticianry.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.034
GPT teacher head0.389
Teacher spread0.356 · 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 teacher head, 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

Citations4
Published2011
Admission routes1
Has abstractyes

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