Effect of anterior capsule polish on visual function: A meta-analysis
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between anterior capsule polish and visual function. METHODS: Data were obtained from Pubmed, Embase, Web of Science, WanFang, VIP and CNKI up to the end of May 2018, without any date or language restrictions for trials. The modified Jadad scale and the newcastle-ottawa scale were used to assess the quality of included studies. Uncorrected visual acuity (UCVA) and posterior capsule opacification (PCO) were used as outcome variables. Data on anterior capsule polish were pooled using weighted, random-effect meta-analysis. RESULTS: One randomized controlled trial and 4 observational cohort studies involving 2533 patients were included in the analyses. There was a statistically significant difference of UCVA (OR 1.92, 95% CI 1.41-2.61) between the polish group and the control group, indicating that anterior capsule polish improved UCVA. Further studies with continuous data also suggested that anterior capsule polish was associated with good UCVA (MD 0.11, 95% CI 0.06-0.16). Posterior capsule opacification rate for 1-year or longer follow-up were extracted for 2561 eyes in 3 studies. Posterior capsule opacification rate was lower in the anterior capsule polish group according to summary odds ratio on PCO rate (OR 0.42 95% CI 0.24-0.73). CONCLUSIONS: Anterior capsule polish prevents complication of modern cataract surgery and benefits on visual function in short term follow-up period.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".