Effectiveness of cataract surgery in reducing driving-related difficulties: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To assess the effects of cataract surgery in improving vision and driving performance while reducing driving-related difficulties. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Twelve electronic databases were searched from the date of inception of each database to May 2007. Other sources of potentially relevant information were also identified and examined. REVIEW METHODS: Eligible study designs included randomized controlled trials (RCT), non-RCT, quasi-experimental, case-control, controlled-before-and-after, and cohort studies that examined driving-related indicators in patients undergoing cataract surgery. MAIN OUTCOME MEASURES: The outcome measures included any type of driving-related indicator. A secondary outcome measure was motor vehicle (MV) crash involvement. RESULTS: Seven studies were included in the review and five in the meta-analysis. The overall pooled odds ratio (OR) was 0.12 (95% CI 0.10 to 0.16). Results suggest that the risk of driving-related difficulties was reduced by 88% following cataract surgery. CONCLUSIONS: Cataract surgery is associated with an 88% reduction in the risk of driving-related difficulties. This supports the efficacy of cataract surgery to improve driving in older people, as well as positive implications for a reduction in MV crashes, overall traffic safety, and individual well-being.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".