Comparison of Goldmann applanation and Ocular Response Analyser tonometry: intraocular pressure agreement and patient preference
Bibliographic record
Abstract
OBJECTIVES: To evaluate the agreement between Goldmann applanation tonometry (GAT) and Ocular Response Analyser (ORA) intraocular pressure (IOP) measurements, and patients' preferences. METHODS: Both eyes of participants in the 'Glaucoma within the Northern Ireland Cohort for the Longitudinal Study of Ageing' (GwNICOLA) were included. Participants underwent GAT by a glaucoma expert and ORA tonometry in a random order. Investigators were masked to measurements between devices. Participants were asked which tonometer, if any, they would prefer. We estimated the 95% limits of agreement (95% LoA) and the variables that influence agreement between tonometers. RESULTS: There were 228 eyes of 120 participants included in this study. Mean age of participants was 68.0 years (SD 8.79) and 52.5% were female. For GAT-ORA IOPcc the mean difference with GAT (95% CI) was -0.23 mmHg (-0.57 mmHg, 0.11 mmHg) and the 95% LoA (95% CIs) were from 4.82 mmHg (5.15 mmHg, 4.48 mmHg) to -5.28 mmHg (-5.61 mmHg, -4.94 mmHg). 40.8% of eyes had an IOP difference of 2 mmHg or more between GAT and ORA IOPcc. Corneal resistance factor (CRF) as estimated by ORA influenced the agreement between GAT and ORA IOPcc. There were no differences in preference for method of tonometry. CONCLUSIONS: Although ORA IOPcc measurements with ORA did not show significant bias compared with GAT, the relatively large proportion of measurement differences between ORA IOPcc and GAT that were >2 mmHg indicates that GAT and ORA IOP measurements may not be interchangeable. There were no differences in preference for method of tonometry.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".