Accuracy of Orbscan pachymetry measurements and DHG ultrasound pachymetry in primary laser in situ keratomileusis and LASIK enhancement procedures
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".