Vision improvement and reduction in falls after expedited cataract surgery
Bibliographic record
Abstract
PURPOSE: To quantify the benefits of expedited cataract surgery in improving visual acuity and reducing fall-related injuries in the older population. SETTING: Developmental Neurosciences and Child Health: Neurons to Neighbourhoods, Vancouver, British Columbia, Canada. METHODS: A systematic review of the literature was conducted. Studies were included if expedited cataract surgery was presented as a measure to enhance vision and to reduce injury. Published and unpublished studies with any type of study design were included. Studies were identified from 12 databases including Medline (1950 to 2008) and Embase (1980 to 2008). The metaanalysis was specific to randomized controlled trials (RCTs). RESULTS: The review comprised 737 participants. Sufficient data for the metaanalysis were available to evaluate the impact of expedited cataract surgery on improved visual acuity and a reduced fall rate. Twenty-two publications that included RCTs and prospective cohort studies met the inclusion criteria. Three studies evaluated visual acuity after expedited routine cataract surgery and routine cataract surgery. The pooled estimate showed that expedited cataract surgery increased visual acuity by more than 7 times (odds ratio [OR], 7.22; 95% confidence interval [CI], 3.16-16.55; P<.0001). Pooling of data from 2 RCTs of 535 participants showed a nonsignificant reduction in the incidence of falls after expedited cataract surgery (OR, 0.81; 95% CI, 0.55-1.17). CONCLUSIONS: Accumulating evidence indicates that expedited cataract surgery is effective in significantly enhancing vision but is inconclusive in preventing falls. FINANCIAL DISCLOSURE: No author has a financial or proprietary interest in any material or method mentioned.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".