Agreement of ocular response analyzer cornea compensated IOP with corvis ST biomechanical IOP following Femtosecond Laser-assisted LASIK
Bibliographic record
Abstract
OBJECTIVES: To compare intraocular pressure (IOP) measurement by ORA-IOPcc and Corvis-bIOP after femtosecond laser-assisted LASIK (FS-LASIK). METHODS: In this prospective cohort study, 56 eyes from 56 consecutive patients scheduled for FS-LASIK were enrolled. All patients had IOP measurement with ORA and Corvis ST by two blinded independent expert examiners. IOP examinations were conducted between 8 and 11 A.M. Data were collected at baseline and 3 months after FS-LASIK. RESULTS: The mean age of the participants was 29.1 ± 6.3 years, and 42 (75%) were female. The average of central corneal thickness (CCT) decreased from 537 ± 23 µm at baseline to 458 ± 31 µm after FS-LASIK. The mean postoperative change of IOP was 0.0 ± 2.1 for bIOP and -2.5 ± 3.2 mmHg for IOPcc. The corresponding 95% limits of agreement (LoA) was -4.1 to 4.1 mmHg and -3.8 to 8.8 mmHg, respectively. Both methods showed no significant correlation between ∆IOP and ∆CCT. The 95% LoA between bIOP and IOPcc after FS-LASIK was -4.8 to 9.1 mmHg. CONCLUSIONS: Compared to the ORA-IOPcc, the Corvis-bIOP showed less variation after FS-LASIK and might be a more appropriate choice for measuring IOP in this condition. The agreement of bIOP vs. IOPcc after FS-LASIK is below the clinically acceptable level, and the two methods could not be regarded as interchangeable.
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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 | 0.005 |
| 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".