Bibliographic record
Abstract
Summary Purpose Does the IOL implanted at cataract surgery affects the driving habits and crash risk of patients. Methods Retrospective analysis of patients who met the visual requirements for a drivers license and had bilateral implantation of the same lens. The patients had at least 2 years of follow‐up. Two groups of patients were identified; each with one of two types of acrylic IOLs. Both groups were given the Driving Habits Questionnaire, by a single investigator. Results 90 patients participated; 51 had acrylic IOL type A and 39 had acrylic IOL type B. The demographics were similar for age, sex, diabetes, glaucoma and IOL power implanted. Group A was more likely to have road traffic accidents (P = 0.066) and less likely to drive at the same speed or faster than general flow of traffic (P = 0.094). Group A to be less likely to have travelled beyond their immediate neighbourhood, to be less likely to rate their quality of driving at average or above and to be more likely to have difficulty driving at night; but this did not reach significance. Conclusions At 2 years postoperatively, the choice of IOL implanted at the time of cataract surgery may have an impact on driving habit and crash risk.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".