Factors affecting central corneal thickness measurement agreement between Scheimpflug imaging and ultrasound pachymetry in keratoconus
Bibliographic record
Abstract
PURPOSE: To evaluate the agreement of central corneal thickness (CCT) measurement between Scheimpflug imaging and ultrasound (U/S) pachymetry in keratoconic eyes, and investigate factors that affect the agreement. METHODS: This post hoc analysis within a prospective, observational non-randomised study preformed at the Kensington Eye Institute, Toronto, Ontario, Canada, included crosslinking candidates with progressive keratoconus (KC). Main outcome measures were the agreement of CCT measurement between Scheimpflug imaging (Pentacam Oculus, Wetzlar, Germany) and U/S pachymetry (PachPen 24-5100, Accutome Inc., Malvern, Pennsylvania, USA), and factors that affect the agreement. RESULTS: A total of 794 keratoconic eyes of 456 subjects with a mean age of 27.6±8.0 years (69.7% males and 49.6% right eyes) were included. Agreement between devices was moderate (intraclass correlation coefficient: 74.9%, Bland-Altman limits of agreement: -48.5 μm to +62.5 μm). In a multivariable analysis, cone decentration (p<0.001, coefficient +10.13 [+6.73 to +13.53 95% CIs]) and Kmax (p<0.001, coefficient +0.68 [+0.46 to +0.90 95% CIs]) were significantly associated (both clinically and statistically) with the level of agreement between the devices; the discrepancy in CCT between the devices increased on average by 10.13 μm for every mm of cone decentration, and by 6.8 μm for every 10D of Kmax. Age, corneal astigmatism and spherical equivalent were statistically but not clinically significant factors affecting agreement. CONCLUSION: The agreement of CCT measurement between Scheimpflug imaging and U/S pachymetry in KC was moderate. To ensure the safety of crosslinking in keratoconic corneas, Scheimpflug and U/S CCT measurements should not be used interchangeably, especially in steep corneas and corneas with decentred cones.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".