Accuracy of Orbscan pachymetry measurements and DHG ultrasound pachymetry in primary laser in situ keratomileusis and LASIK enhancement procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To assess the accuracy and variability in pachymetry measurements obtained by Orbscan and by DGH ultrasound in primary laser in situ keratomileusis (LASIK) and LASIK enhancement procedures. SETTING: Gimbel Eye Centre, Calgary, Alberta, Canada. METHODS: A retrospective analysis of 906 consecutive primary LASIK and 183 consecutive LASIK enhancement procedures for which preoperative DGH ultrasound and Orbscan pachymetry measurements were obtained. The theoretical residual corneal thickness was compared to measurements by both instruments in 60 eyes that had primary procedures and enhancements. RESULTS: In primary LASIK eyes, the DGH ultrasound measurements were thicker than the Orbscan measurements by a mean of 18.4 microm +/- 17.4 (SD). The DGH ultrasound measurements were thicker than the Orbscan measurements by a mean of 50.1 +/- 40.7 microm in preenhancement pachymetry measurements. The DGH ultrasound measurements were consistent with theoretical residual corneal thickness, 493.0 +/- 42.0 microm versus 487.0 +/- 31.0 microm (P =.65), while Orbscan measurements were statistically less than the theoretical residual corneal thickness, 431.0 +/- 42.0 microm versus 468.0 +/- 39.0 microm (P =.0001). CONCLUSION: DGH ultrasound was a more accurate measurement of corneal pachymetry than Orbscan. The discrepancies between DGH ultrasound and Orbscan pachymetry measurements were more prominent in eyes that had had LASIK.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it