Transient Increase of Flicker Electroretinography Amplitudes after Cataract Surgery
Bibliographic record
Abstract
Purpose To determine the characteristics and cause of the increase in the amplitude of flicker electroretinography (ERG) after cataract surgery. Design Prospective, observational clinical study. Participants Thirty patients who underwent cataract surgery. Methods Flicker ERGs were recorded with the RETeval system without mydriasis. The central macular thickness (CMT) was measured by OCT and the aqueous flare value (AFV) by laser flare-cell photometry. These examinations were performed before surgery and 1 day, 1 week, 1 month, 2 months, and 3 months after surgery. Linear regression analysis through the origin was used to compare the correlations between the relative changes in flicker ERG amplitudes and the changes in the CMT and AFV at different times after the surgery. Main Outcome Measures The amplitude of flicker ERGs, CMT, and AFV. Results The mean amplitude of flicker ERGs increased significantly by 31% at 1 week after surgery ( P < 0.001); a significant increase in the amplitudes was not present at 3 months after the surgery. The mean AFV was significantly increased at 1 day after surgery ( P < 0.001), and the CMT was significantly increased at 1 to 3 months after surgery ( P < 0.001). The changes in flicker ERG amplitudes at 1 week after surgery were significantly associated with the changes in the CMT at 1 to 3 months after surgery ( P < 0.05), and they were weakly associated with the changes in AFV at 1 day after surgery ( P = 0.05). Conclusions These results suggest that the increase in the amplitude of flicker ERGs after cataract surgery is a transient phenomenon that has a peak at 1 week after surgery. The increase of flicker ERG amplitude was associated with measures that are frequently used to evaluate postoperative inflammation. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".