AQUEOUS HUMOR CYTOKINE LEVELS AS BIOMARKERS OF DISEASE SEVERITY IN DIABETIC MACULAR EDEMA
Bibliographic record
Abstract
PURPOSE: To determine whether aqueous cytokine levels correlate with disease severity in diabetic macular edema. METHODS: A prospective cross-sectional study of 49 adults with diabetes mellitus, centre-involving diabetic macular edema and central subfield macular thickness ≥310 μm on spectral domain optical coherence tomography. Clinical examination and aqueous sampling were carried out before an initial injection of ranibizumab. Multiplex immunoassay of sample was carried out for vascular endothelial growth factor, placental growth factor, transforming growth factor beta, intercellular adhesion molecule-1, interleukin (IL)-2, IL-3, IL-6, IL-8, IL-10, IL-17, vascular cell adhesion molecule-1, monocyte chemoattractant protein-1, and epidermal growth factor. Multivariate robust regression models were constructed, and adjusted for age, lens status, or severity of retinopathy, and size of foveal avascular zone. RESULTS: Spectral domain optical coherence tomography macular volume was an excellent measure of disease severity, correlating strongly with central subfield macular thickness (P < 0.001), best-corrected Snellen visual acuity (P < 0.001), and baseline diabetic retinopathy severity (P = 0.01). Elevated aqueous intercellular adhesion molecule-1 correlated with greater macular volume (P = 0.002). No aqueous cytokine, including VEGF, correlated with central subfield macular thickness. There was an association between IL-10 levels and best-corrected Snellen visual acuity (P = 0.03). CONCLUSION: Aqueous intercellular adhesion molecule-1 correlates with disease severity as measured by macular volume on spectral domain optical coherence tomography, and IL-10 is associated with BCVA. Intercellular adhesion molecule-1 may be a clinically useful biomarker for diabetic macular edema severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".