Prospective Comparative Analysis of 4 Different Intraocular Pressure Measurement Techniques and Their Effects on Pressure Readings
Bibliographic record
Abstract
PURPOSE: To compare intraocular pressure (IOP) measurement using the Goldmann applanation tonometry (GAT) without fluorescein, with fluorescein strips, with fluorescein droplets, and IOP measurement with Tono-Pen Avia (TPA). PATIENTS AND METHODS: This was a prospective comparative clinical analysis. It was performed in clinical practice. The study population consisted of 40 volunteer patients, 1 eye per patient. All patients who were 18 years and older having routine ophthalmological examination were eligible to participate. Active corneal abrasions and/or ulcers, previous glaucoma surgery, or prostheses interfering with GAT measurement were excluded. GAT IOP was measured first without fluorescein, then with fluorescein strip, then with fluorescein droplet, and finally with the TPA device. The main outcome measure was central corneal IOP. RESULTS: Mean±SD IOP measurements for GAT without fluorescein, with fluorescein strip, with fluorescein droplet, and for TPA groups were 12.65±3.01, 14.70±2.82, 15.78±2.64, and 16.33±3.08 mm Hg, respectively. Repeated-measures analysis of variance corrected with the Greenhouse-Geisser estimate ([Latin Small Letter Open E]=0.732) showed that measuring technique had a significant effect on IOP measurements (F2.20,85.59=34.66, P<0.001). The pairwise post hoc testing showed statistically significant mean differences (P≤0.001) between all techniques except when GAT with fluorescein droplet was compared with TPA (P=0.222). The Bland-Altman analyses showed 95% limits of agreement maximum potential discrepancies in measurement ranging from 5.89 mm Hg in the GAT with fluorescein strip versus droplet compared with 11.83 mm Hg in the GAT with fluorescein strip versus TPA comparison. CONCLUSIONS: IOP measurement technique significantly impacted the values obtained. The ophthalmologist should ensure consistent measurement technique to minimize variability when following patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".