A METHOD OF REPORTING MACULAR EDEMA AFTER CATARACT SURGERY USING OPTICAL COHERENCE TOMOGRAPHY
Bibliographic record
Abstract
In Brief Objective: To validate a method of reporting postcataract macular edema (ME) using optical coherence tomography (OCT). Methods: Data were analyzed for 130 eyes followed prospectively for ME after uncomplicated cataract surgery. Each eye underwent OCT within 4 weeks before surgery and at 1 month and 3 months after surgery. ME was defined by observation of cystoid changes by OCT. Results: Incidence of ME was 14% (95% confidence interval, 8–20). Average increase in baseline center point thickness (CPT) ± SD at 1 month for eyes with and without ME was 202 ± 113 μm and 8 ± 19 μm, respectively (P < 0.001), which resulted in a 1-letter loss (−0.02 logMAR [logarithm of the minimum angle of resolution]) and a 3-line gain (0.29 logMAR) in vision, respectively (P < 0.001). Percent change in baseline CPT ± SD for eyes with and without ME was 115 ± 67% and 6 ± 11%, respectively (P < 0.001). A ≥40% increase in baseline CPT accurately determined 100% of eyes with ME and 99% of eyes without ME. Conclusions: A ≥40% increase in baseline CPT, determined by OCT, offers a valid and objective method of reporting clinically relevant postcataract ME. Standardized reporting of postcataract ME would allow objective assessment and comparison of treatment outcomes among clinical studies. A ≥40% increase in baseline center point thickness, determined by optical coherence tomography, may offer a valid and objective method of reporting postcataract macular edema. Standardized reporting of postcataract macular edema would allow objective assessment and comparison of treatment outcomes among clinical studies.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".