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Record W2133294547 · doi:10.1080/09638280600621436

Impact of visual impairment on service and device use by individuals with age-related macular degeneration (AMD)

2006· article· en· W2133294547 on OpenAlexafffund
Jordana K. Schmier, Michael T. Halpern, David Covert, Judith Delgado, Sanjay Sharma

Bibliographic record

VenueDisability and Rehabilitation · 2006
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsQueen's UniversityHotel Dieu Hospital
FundersAGE-WELL
KeywordsMacular degenerationVisual impairmentDegeneration (medical)MedicineOptometryPhysical medicine and rehabilitationAudiologyGerontologyPsychologyOphthalmologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: To assess the patient-reported use of services, supplements, and devices among individuals with age-related macular degeneration (AMD) and evaluate the impact of visual impairment level on this use. METHOD: Data for this study were collected using two instruments, the AMD Health and Impact Questionnaire and the Daily Living Tasks Dependent on Vision questionnaire (DLTV). Both questionnaires were mailed to members of the Macular Degeneration Partnership. The study was approved by an IRB and respondents provided consent before participating. Respondents' visual acuity (VA) was estimated using scores from the DLTV, while use of services and devices was collected from the AMD Questionnaire. De-identified data were analysed in SAS. RESULTS: Of 803 respondents, 56% were male and the mean age was 73 years. Use of services (e.g., counseling, rehabilitation), and devices significantly increased as VA decreased. Using standard US costs, costs for services, supplements, and devices ranged from 506-1619 US dollars depending on VA. CONCLUSION: There are substantial differences in service and device use with increased AMD severity. Delaying progression of AMD could result in considerable cost savings.

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 score0.010
Threshold uncertainty score0.505

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 studies0.0000.001
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 head0.014
GPT teacher head0.331
Teacher spread0.317 · 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

Citations20
Published2006
Admission routes2
Has abstractyes

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