Exploring the role of retinal fluid as a biomarker for the management of diabetic macular oedema
Bibliographic record
Abstract
Anti-VEGF therapies are associated with significant gains in visual acuity and fluid resolution in the treatment of diabetic macular oedema (DMO) and have become the standard of care. However, despite their efficacy, outcomes can be unpredictable, vary widely between individual eyes, and a large proportion of patients have persistent fluid following initial treatment, with a negative impact on visual outcomes. Anatomical parameters measured by optical coherence tomography (OCT), in addition to visual acuity, are key to monitoring treatment effectiveness and guiding retreatment decisions; however, existing guidelines on the management of DMO lack clear recommendations for interpretation of OCT parameters, or proposed thresholds of various markers to guide retreatment decisions. Although central subfield thickness (CSFT) has been widely used as a marker for retreatment decisions in clinical trials in DMO, and a reduction in CSFT has generally been shown to accompany improvements in best-corrected visual acuity with treatment, analyses of the relationship between these parameters show that the correlation is small to moderate. A more direct relationship can be seen between an increased magnitude of CSFT fluctuations over time and poorer visual acuity, suggesting that control of CSFT could be important in maximising visual outcomes. The relationship between visual outcomes and qualitatively assessed intraretinal fluid and subretinal fluid is also unclear, although quantitative assessments of fluid parameters suggest that untreated intraretinal fluid and subretinal fluid negatively impact visual outcomes. These findings highlight a need for clearer guidelines on the management of retinal fluid to improve visual outcomes for patients with DMO.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".