Visual Acuity at 6 Weeks after Small Incision Cataract Surgery and Role of Audit in Predicting Visual Acuity
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: We present the best-corrected visual acuity (BCVA) at 6 weeks after small incision cataract surgery (SICS) and review the role of audit in predicting visual acuity (VA). METHODS: This was a retrospective data analysis of 14,393 SICS performed during 2007-2008 at a hospital in central India. Ophthalmologists noted preoperative, operative, and postoperative details. The BCVA before and 1 day and 6 weeks after surgery were noted. We identified factors associated with BCVA at 1-day and 6-week follow-up. RESULTS: Six weeks after surgery, 12,522 (87%) and 1473 (10.2%) patients had BCVA > or =6/18 and 6/24-6/60, respectively. Vision improved between 2 follow-ups in 6695 eyes (46.5% (95% confidence interval (CI) 45.7-47.3)), remained the same in 7117 eyes (49.4%), and deteriorated in 544 (3.8%) eyes. BCVA at 6 weeks was negatively associated with blindness (VA <3/60 in the better eye) before surgery (odds ratio (OR) = 0.73, 95% CI 0.58-0.92), surgeon's experience (OR = 0.75, 95% CI 0.71-0.81), and male patients (OR = 0.73, 95% CI 0.67-0.80). BCVA at 6 weeks was positively associated with older age (OR = 1.02, 95% CI 1.01-1.03) and intraoperative complications (OR = 1.44, 95% CI 1.14-1.83). The association of VA <6/60 1 day after surgery with improved vision between the 2 follow-ups was not statistically significant (OR = 0.005, p = 0.98). CONCLUSIONS: BCVA at 6 weeks after SICS was > or =6/18 in 87% of operated eyes. By performing surgical audit, one can identify high-risk groups that need proactive subsequent follow-ups.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it