Visual outcomes associated with optical coherence tomography biomarkers in diabetic macular edema: A systematic review
Bibliographic record
Abstract
We evaluated the value of baseline structural optical coherence tomography (OCT) biomarkers in determining functional treatment response at 6,12-, and 24-months following treatment initiation in patients undergoing anti-vascular endothelial growth factor, steroid, or laser treatment for diabetic macular edema (DME). Ovid MEDLINE, EMBASE, and Web of Science databases were searched from inception for studies evaluating the association between baseline OCT biomarkers and best-recorded central visual acuity (VA). A synthesis using vote counting based on the direction of effect in relation to a five-letter minimally important difference was used. GRADE guidelines informed the certainty of evidence. Ninety-six studies evaluating 29 biomarkers were included. No biomarker with at least 'Low' certainty was associated with improved VA. Greater baseline ellipsoid zone (EZ) disruption and hyperreflective foci were associated with decreased VA at 6 months with "Low" certainty of evidence. There was also "Low" certainty that increased baseline disorganization of the retinal inner layers, increased baseline disruption of the external limiting membrane or EZ were associated with decreased VA at 12 months. No biomarker was associated with a poor prognosis with at least 'Moderate' certainty. The certainty was downgraded due to inadequately controlling for confounders and a lack of standardization regarding how biomarkers were defined and outcomes were measured.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".