Limits of Agreement Between the Optical Pachymeter and a Noncontact Specular Microscope
Bibliographic record
Abstract
PURPOSE: To determine the limits of agreement between central corneal thickness (CCT) measurements made with the slit lamp-attached optical pachymeter and the SP2000P noncontact specular microscope. METHODS: Triplicate readings for CCT were obtained for each of 130 (right) eyes of 130 patients, using the slit lamp-attached optical pachymeter and then the SP2000P noncontact specular microscope. The average CCT measured by each method was compared. Subsequently, the mean difference between both sets of measurements was assessed, and the 95% confidence interval (limits of agreement) between both techniques was determined. RESULTS: The mean +/- SD CCT measured by the optical pachymeter was 543 +/- 34 microm and 532 +/- 34 microm for the specular microscope. We found a statistically significant (P < 0.001) mean bias of 10 mum between CCT values measured with both types of equipment, with the optical pachymeter returning the higher values. The coefficient of variation was 6.3% for the optical pachymeter and 6.4% for the specular microscope. CONCLUSIONS: The right eye CCT measurements made by the optical pachymeter are, on average, 10 mum thicker than those made with the SP2000P specular microscope, which suggests that both pieces of equipment cannot be used interchangeably to monitor CCT changes in patients. Excluding left eye measurements, the reliability of the optical pachymeter is identical to that of the noncontact specular microscope.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".