Assessment of Factors Affecting the Difference in Intraocular Pressure Measurements Between Dynamic Contour Tonometry and Goldmann Applanation Tonometry
Bibliographic record
Abstract
PURPOSE: To determine if the difference in intraocular pressure (IOP) measurements between dynamic contour tonometry (DCT) and Goldmann applanation tonometry (GAT) is correlated with axial length (AL), and to assess the possible influence of age, sex, central corneal thickness (CCT), corneal hysteresis (CH), ocular pulse amplitude (OPA), and glaucoma status on the difference in IOP measurements between the 2 instruments (ΔIOP=DCTIOP- GATIOP). METHODS: Two hundred sixty-oneparticipants (509 eyes) in these 4 groups were included: 53 normal individuals (N; 106 eyes), 112 glaucoma patients (OAG; 212 eyes), 52 glaucoma suspects (GS; 103 eyes), and 44 patients with ocular hypertension (OHT; 88 eyes). The patients who had had an incisional ocular surgery were excluded. All participants underwent IOP evaluation with DCT and GAT and AL, CCT, and CH measurements. The influence of age, sex, AL, CCT, CH, OPA, and glaucoma diagnostic status on ΔIOP was evaluated using correlation analysis and analysis of variance (ANOVA). Right (OD) and left eyes (OS) were analyzed separately. RESULTS: ΔIOP was higher in eyes with longer axial lengths (OD: r=0.142, P=0.02; OS: r=0.233, P<0.001). ΔIOP also correlated with CH (OD: r=-0. 127, P=0.04; OS: r=-0.169, P=0.01), in which the ΔIOP increased as CH decreased (corresponding to less rigid corneas). OPA also correlated negatively with ΔIOP, but the correlation was only statistically significant in left eye (OD: r=-0.112, P=0.08; OS: r=-0.124, P=0.05). Age, CCT, sex, and diagnostic status did not influence ΔIOP significantly. CONCLUSIONS: GAT underestimated IOP more compared with DCT in patients with longer axial length and in patients with lower corneal hysteresis.
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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 | 0.002 | 0.010 |
| 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".