Comparison of central corneal thickness measurements by specular microscopy, ultrasound pachymetry, and ultrasound biomicroscopy
Bibliographic record
Abstract
PURPOSE: To compare the reproducibility and mean values of central corneal thickness (CCT) obtained by specular microscopy, ultrasound pachymetry, and ultrasound biomicroscopy (UBM). SETTING: Department of Ophthalmology, University of Toronto, Toronto, Ontario, Canada. METHODS: Thirty-one healthy volunteers were recruited for a sample size of 62 eyes. All subjects had pachymetric measurements by specular microscopy, ultrasound pachymetry, and UBM. Three separate measurements meeting criteria for centrality and perpendicularity were recorded for each eye. RESULTS: The mean CCT by specular microscopy was 572 microm (95% confidence interval (CI), 566-578 microm), which was significantly greater than 550 microm (95% CI, 545-556 microm) (P<.001) and 555 microm (95% CI, 550-560 microm) (P<.001) by ultrasound pachymetry and UBM, respectively. The mean standard deviation (SD) of repeated measurements by specular microscopy was 7.82 microm, which was significantly greater than the mean SDs of 4.14 microm (P<.001) and 3.90 microm (P<.001) by ultrasound pachymetry and UBM, respectively. There was no statistically significant difference between the mean SDs by ultrasound pachymetry and UBM (P=.156). CONCLUSIONS: Although the CCT measurements by specular microscopy were significantly less reproducible than those by ultrasound pachymetry and UBM, the error levels were clinically acceptable. Both ultrasound pachymetry and UBM produced similar CCT measurements, which were significantly less than those generated by specular microscopy. One should be aware of the advantages and limitations of each machine and of possible differences in the CCT measurements by optical and ultrasound pachymetry.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".