A pilot study to determine if intraocular lens choice at the time of cataract surgery has an impact on patient-reported driving habits
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".