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Record W4280532957 · doi:10.3390/jcm11102809

Sjögren’s Syndrome Associated Dry Eye: Impact on Daily Living and Adherence to Therapy

2022· article· en· W4280532957 on OpenAlexaffabout
Evan Michaelov, Caroline G. McKenna, Pierre Ibrahim, Manav Nayeni, Arpit Dang, Rookaya Mather

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineDry eyesGerontologyInternal medicineOphthalmology

Abstract

fetched live from OpenAlex

Sjögren's syndrome-related dry eye disease (SS-DED) often involves more severe dry eye symptoms than people with non-SS dry eye disease (DED). This cross-sectional study employed an anonymous self-administered questionnaire to understand the experience of people living with SS-DED and to identify factors affecting adherence to DED self-care. Participants reported difficulty with visual tasks such as driving, and diminished enjoyment in daily activities due to DED symptoms. Almost 80% reported being worried about a reduced quality of life due to DED, and over 50% reported fear of blindness. The most common reasons for non-adherence were cost of therapy and forgetting to instill drops. Drop rationing to reduce cost of therapy was endorsed by 83% of respondents. Only 3% of respondents had private insurance for non-prescription agents required to treat DED. A quarter of respondents reported they would not disclose non-adherence to their eye care provider. Multiple regression analysis revealed age was a significant contributor to missing drops. This is the first study to report on the financial burden experienced by SS-DED patients in Canada. This paper identified strategies used by patients to reduce the cost of therapy and its impact on adherence to treatment. Patients may be reluctant to disclose challenges regarding adherence to DED therapy, as well as fears of worsening quality of life.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.428
Teacher spread0.360 · 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.

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

Citations15
Published2022
Admission routes2
Has abstractyes

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