Summary of Glaucoma Diagnostic Testing Accuracy: An Evidence-Based Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: New glaucoma diagnostic technologies are penetrating clinical care and are changing rapidly. Having a systematic review of these technologies will help clinicians and decision makers and help identify gaps that need to be addressed. This systematic review studied five glaucoma technologies compared to the gold standard of white on white perimetry for glaucoma detection. METHODS: OVID(®) interface: MEDLINE(®) (In-Process & Other Non-Indexed Citations), EMBASE(®), BIOSIS Previews(®), CINAHL(®), PubMed, and the Cochrane Library were searched. A gray literature search was also performed. A technical expert panel, information specialists, systematic review method experts and biostatisticians were used. A PRISMA flow diagram was created and a random effect meta-analysis was performed. RESULTS: A total of 2,474 articles were screened. The greatest accuracy was found with frequency doubling technology (FDT) (diagnostic odds ratio (DOR): 57.7) followed by blue on yellow perimetry (DOR: 46.7), optical coherence tomography (OCT) (DOR: 41.8), GDx (DOR: 32.4) and Heidelberg retina tomography (HRT) (DOR: 17.8). Of greatest concern is that tests for heterogeneity were all above 50%, indicating that cutoffs used in these newer technologies were all very varied and not uniform across studies. CONCLUSIONS: Glaucoma content experts need to establish uniform cutoffs for these newer technologies, so that studies that compare these technologies can be interpreted more uniformly. Nevertheless, synthesized data at this time demonstrate that amongst the newest technologies, OCT has the highest glaucoma diagnostic accuracy followed by GDx and then HRT.
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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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.099 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".