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Record W3210662376 · doi:10.1016/j.ajo.2021.10.023

Development and Validation of a Visual Symptom–Specific Patient-Reported Outcomes Instrument for Adults With Cataract Intraocular Lens Implants

2021· article· en· W3210662376 on OpenAlexaboutno aff
Kathryn Lasch, James Marcus, Caroline Seo, Kelly P. McCarrier, R. Wirth, Donald L. Patrick, John F. O'Riordan, Renea Stasaski

Bibliographic record

VenueAmerican Journal of Ophthalmology · 2021
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersAlcon
KeywordsMedicineCataractsOptometryIntraocular lensGLAREConstruct validityOphthalmologyMultifocal intraocular lensCataract surgeryIntraocular lensesPatient satisfactionVisual acuityPhacoemulsificationSurgery

Abstract

fetched live from OpenAlex

PURPOSETo develop a patient-reported outcome measure for capturing visual and ocular symptoms before and after implantation of intraocular lenses (IOLs) for treatment of cataracts.DESIGNQuestionnaire development and validation study.METHODSThe Questionnaire for Visual Disturbances (QUVID) was developed based on a literature and instrument review; 13 clinician interviews among ophthalmologists in the United States and Europe; and 67 hybrid qualitative patient interviews among adult patients in the United States and Australia before and/or after monofocal, traditional multifocal, or trifocal IOL implantation. Assessment of the QUVID's psychometric properties was conducted via a noninterventional cross-sectional study of previously treated cataract patients in the United States, Canada, and Australia (n = 150), and assessment of ability to detect meaningful change via 2 pivotal US clinical trials among patients with trifocal or extended vision IOL compared with monofocal IOL controls (n = 457).RESULTSThe QUVID includes subitems about the bothersomeness of 7 visual symptoms: starburst, halo, glare, hazy vision, blurred vision, double vision, and dark areas. The postoperative version contains 1 item asking the respondents whether their symptoms bothered them enough to want another surgery, if the IOL was the cause.CONCLUSIONSThe QUVID was reviewed by the US Food and Drug Administration and found appropriate as a fit-for-purpose measure, demonstrating requisite evidence for content validity, construct validity, reliability, and ability to detect change. To develop a patient-reported outcome measure for capturing visual and ocular symptoms before and after implantation of intraocular lenses (IOLs) for treatment of cataracts. Questionnaire development and validation study. The Questionnaire for Visual Disturbances (QUVID) was developed based on a literature and instrument review; 13 clinician interviews among ophthalmologists in the United States and Europe; and 67 hybrid qualitative patient interviews among adult patients in the United States and Australia before and/or after monofocal, traditional multifocal, or trifocal IOL implantation. Assessment of the QUVID's psychometric properties was conducted via a noninterventional cross-sectional study of previously treated cataract patients in the United States, Canada, and Australia (n = 150), and assessment of ability to detect meaningful change via 2 pivotal US clinical trials among patients with trifocal or extended vision IOL compared with monofocal IOL controls (n = 457). The QUVID includes subitems about the bothersomeness of 7 visual symptoms: starburst, halo, glare, hazy vision, blurred vision, double vision, and dark areas. The postoperative version contains 1 item asking the respondents whether their symptoms bothered them enough to want another surgery, if the IOL was the cause. The QUVID was reviewed by the US Food and Drug Administration and found appropriate as a fit-for-purpose measure, demonstrating requisite evidence for content validity, construct validity, reliability, and ability to detect change.

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 score0.172
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.031
GPT teacher head0.332
Teacher spread0.301 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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