A METHOD OF REPORTING MACULAR EDEMA AFTER CATARACT SURGERY USING OPTICAL COHERENCE TOMOGRAPHY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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