Prevalence and clinical implications of subretinal fluid in retinal diseases: a real-world cohort study
Bibliographic record
Abstract
BACKGROUND/AIMS: To characterise the baseline prevalence of subretinal fluid (SRF) and its effects on anatomical and visual acuity (VA) outcomes in diabetic macular oedema (DME) and retinal vein occlusion (RVO) following anti-vascular endothelial growth factor (VEGF). METHODS: This is a retrospective cohort study of 122 DME and 54 RVO patients who were initiated on anti-VEGF therapy with real-world variable dosing. The DME and RVO cohorts were subclassified based on the presence of SRF at presentation. Snellen VA was measured and converted to logarithm of the minimum angle of resolution (LogMAR). Changes in VA and central subfield thickness (CST) were assessed up to 24 months. RESULTS: SRF was present in 22% and 41% in DME and RVO patients, respectively. In the DME subcohort, eyes with SRF showed an improvement of 0.166 logMAR (1.7 Snellen chart lines) at 12 months and 0.251 logMAR (2.6 Snellen chart lines) at 24 months, which were significantly greater compared with those of the non-SRF group. A significantly greater reduction in CST was noted in the SRF eyes compared with the non-SRF eyes at 3 months and 1 month in the DME and RVO subcohorts, respectively. CONCLUSION: Baseline SRF is a good marker for a greater reduction in CST in both DME and RVO, but an improvement in VA associated with SRF may be only noted in DME.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".