MétaCan
Menu
Back to cohort
Record W1682996925 · doi:10.2147/opth.s90886

A pilot study to determine if intraocular lens choice at the time of cataract surgery has an impact on patient-reported driving habits

2015· article· en· W1682996925 on OpenAlexaff
George Beiko

Bibliographic record

VenueClinical ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineCataract surgeryIntraocular lensStatistical significanceOphthalmologyDriving simulatorStatistical analysisOptometrySimulationEngineeringInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To determine if intraocular lens (IOL) choice at the time of cataract surgery affects driving habits. MATERIALS AND METHODS: Pseudophakes who were 28-35 months postbilateral cataract surgery with one of two contemporary one-piece hydrophobic acrylic IOLs (SN60WF or ZCB00) were asked to complete the Driving Habits Questionnaire, a validated instrument for determining self-reported driving status, frequency, and difficulty. To determine if there were any differences in driving habits between the two groups, t-tests and χ (2) tests were used. RESULTS: Of 90 respondents, 72 (40 SN60WF and 32 ZCB00) were still active drivers. The SN60WF-implanted subjects were less likely to drive at the same speed or faster than the general flow of traffic, less likely to rate their quality of driving as average/above average, less likely to have traveled beyond their immediate neighborhood, less likely to drive at night, more likely to have moderate-to-severe difficulty driving at night, and more likely to have self-reported road traffic accidents. The differences did not reach statistical significance. CONCLUSION: Changes in patients' driving habits 2-3 years after cataract surgery may be associated with the type of IOL implanted. A larger study, powered to demonstrate statistical significance, is needed to verify the trends identified in this pilot study and discover possible contributing factors.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.120
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.289
GPT teacher head0.468
Teacher spread0.178 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueClinical ophthalmologySame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207