Analyse von corneo-skleralen und biometrischen Messwerten als Prädiktoren für Sklerallinsenparameter: eine systematische Übersicht
Bibliographic record
Abstract
Purpose. In the latest years, the interest in scleral lenses is progressively increasing among practitioners and patients from all around the world. An optimal lens fit is necessary to ensure patient comfort and visual quality. However, it is not straightforward to estimate the appropriate scleral lens parameters for a particular patient. This review paper aims to summarize the current state of knowledge in predictors of scleral lens parameters based on corneo-scleral shape. Material and Methods. Literature was reviewed from PubMed. A total of 33 articles were specifically selected for the current study. Results. Even though not all available corneo-scleral meas- urements may be helpful in the fitting experience, the re- fractive state of the cornea, corneal flattest and steepest keratometry, scleral toricity, and axial length have proven to influence the scleral lens parameters and, consequently, the quality of scleral lens fit. Conclusion. The usefulness of corneal, scleral, and corneo- scleral measures is reviewed and critically evaluated. A special effort was made to highlight the clinical implications of the findings. Keywords Corneal topography, scleral profile, profilometry, myopia progression, axial length, contact lens fitting
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".