Intraocular Pressure Before and After Corneal Refractive Surgery: A Prospective Comparison of Corvis ST and Ocular Response Analyzer
Bibliographic record
Abstract
PRÉCIS: The study showed that Corvis ST's biomechanical intraocular pressure (bIOP) and ocular response analyzer's (ORA) cornea-compensated intraocular pressure (IOPcc) do not agree well, before or after photorefractive keratectomy (PRK), and may not be used interchangeably. bIOP remained unchanged after PRK. OBJECTIVE: To evaluate the agreement between the biomechanically corrected intraocular pressure (bIOP) measured by the Corvis ST and the IOPcc measured by the ORA before and after PRK. PATIENTS AND METHODS: In this prospective interventional study, a total of 53 patients (53 eyes) were included. Measurements were acquired using both the Corvis ST and ORA devices before and 3 months post-PRK. The agreement between the 2 devices was evaluated using limits of agreement (LoA) and Bland-Altman plots. RESULTS: The participants had a mean age of 29.6 ± 5.21 years (range: 21 to 40), with 41 (77.4%) of them being females. After the surgery, the average change in intraocular pressure (IOP) was 0.3 ± 1.7 mm Hg for bIOP and -1.6 ± 4.0 mm Hg for IOPcc. The corresponding 95% LoA were -3.5 to 4.2 mm Hg and -9.5 to 6.3 mm Hg, respectively. The 95% LoA between bIOP and IOPcc after PRK was -2.3 to 8.5 mm Hg. Notably, the bIOP values were higher for IOPs <20 mm Hg and lower for IOPs >20 mm Hg compared with IOPcc. CONCLUSIONS: The findings indicate a weak agreement between the Corvis ST-bIOP and the ORA-IOPcc both before and after PRK. These devices may not be used interchangeably for IOP measurement. bIOP exhibited less variation compared with the IOPcc, suggesting that the bIOP may be a better option for IOP reading after PRK.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".