Optimal Optical Coherence Tomography–Based Measures in the Diagnosis of Clinically Significant Macular Edema
Bibliographic record
Abstract
OBJECTIVE: To compare optical coherence tomography-based measures of retinal thickness and volume as quantitative tests for clinically significant macular edema (CSME). DESIGN: Diagnostic validation study. METHODS: Sixty-five eyes with diabetic retinopathy underwent stereo photographic and optical coherence tomographic examination. Stereo photographs were examined in a masked fashion to determine the presence or absence of CSME according to criteria from the Early Treatment Diabetic Retinopathy Study. Optical coherence tomography-based measurements of central foveal thickness as well as retinal volumes within a series of radii of fixation were generated. The main outcome measures were areas under receiver operating characteristic curves. Likelihood ratios, sensitivities, and specificities for the diagnosis of CSME were also evaluated. RESULTS: Retinal volumes within radii of 0.50 mm and 1.11 mm of fixation and central foveal thickness were the best variables for discriminating between those with and without CSME as evidenced by analysis of receiver operating characteristic curves. There were no significant differences among these 3 variables in their performance as diagnostic tests for CSME. CONCLUSIONS: Optical coherence tomography-based retinal volume and central foveal thickness variables display comparable abilities to discriminate between those with and without CSME. Both measures may have clinical applications as quantitative diagnostic tests for CSME.
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 | 0.008 | 0.033 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".