Scheimpflug imaging for laser refractive surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the principles and clinical applications of Scheimpflug corneal and anterior segment imaging with special relevance for laser refractive surgery. RECENT FINDINGS: Computerized Scheimpflug imaging has been used for corneal and anterior segment tomography (CASTm) in different commercially available instruments. Such approach computes the three-dimensional image of the cornea and anterior segment, enabling the characterization of elevation and curvature of the front and back surfaces of the cornea, pachymetric mapping, calculation of the total corneal refractive power and anterior segment biometry. CASTm represents a major evolution for corneal and anterior segment analysis, beyond front surface corneal topography and single point central corneal thickness measurements. This approach enhances the diagnostic abilities for screening ectasia risk as well as for planning, evaluating the results, managing complications of refractive procedures, and selecting intraocular lens power, type, and design. In addition, dynamic Scheimpflug imaging has been recently introduced for in-vivo corneal biomechanical measurements and has also been used for anterior segment imaging of femtocataract surgery. SUMMARY: Scheimpflug imaging has an important role for laser refractive surgery with different applications, which continuously improve due to advances in technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".