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Record W2412526635

The predictability of retention and discontinuation of contact lenses.

2003· article· en· W2412526635 on OpenAlexaff
Jeffrey W. Jutai, Hy Day, William Woolrich, Graham Strong

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsDiscontinuationPsychosocialPsychologyQuality of life (healthcare)MedicinePhysical therapyOptometryNursingPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this research was to investigate the possibility of predicting the retention or discontinuation of contact lenses on the basis of their effect on the quality of life of the wearer. While most studies of discontinuation of assistive technology devices have focused on other than psychosocial causes of discontinuation, there is reason to believe that a major cause is the psychosocial effect of the device on the user. METHODS: Participants were 418 adult volunteers who had been fitted with contact lenses. One hundred eighty of the participants had discontinued wearing their lenses, while 238 were still wearing their lenses. The participants were asked to complete a recently developed questionnaire, the Psychosocial Impact of Assistive Devices Scales (PIADS), which measures the psychosocial impact of assistive devices, and is used also to predict retention and discontinuation of other assistive devices. RESULTS: Overall correct prediction of the 418 respondents was 68.9%. The PIADS was better at predicting discontinuation than retention among males, while the reverse was true for females. CONCLUSIONS: The PIADS was shown to be a valid instrument to predict retention and discontinuation of contact lenses.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.049
GPT teacher head0.244
Teacher spread0.195 · 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 designBench or experimental
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

Citations26
Published2003
Admission routes1
Has abstractyes

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