On the art and science of rigid contact lens fitting
Bibliographic record
Abstract
Clinical relevance Examination of the literature can help answer the age-old question of the extent to which rigid contact lenses can be considered an art versus a science.Background This work aims to assemble rigid contact lens-related publication metrics to identify the most impactful papers, authors, institutions, countries and journals.Methods A search was undertaken of the Scopus database to identify rigid contact lens-related articles published since this lens type was first described in 1949. The 25 most highly cited papers were determined from the total list of 1,823 papers found. Rank-order lists by count were assembled for the ‘top 25ʹ in each of four categories: authors, institutions, countries and journals. A subject-specific rigid contact lens h-index (hRL-index) was derived for each author, institution, country and journal to serve as a measure of impact in the field. A short list of the top constituents in each category were ranked by hRL-index and tabulated.Results The most highly cited paper (467 citations) is entitled ‘Risk factors and prognosis for corneal ectasia after LASIK’, by Randleman et al. Karla Zadnik (hRL = 20; 32 papers) and Richard Hill (h = 10; 50 papers) are most impactful and prolific authors, respectively. The Ohio State University (hRL = 24; 96 papers) is the most impactful and prolific institution and the United States (hRL = 51; 680 papers) is the most impactful and prolific country. Optometry and Vision Science (hRL = 30; 233 papers) is the most impactful journal.Conclusions Impactful authors, institutions, countries and journals in the field of rigid lenses are identified. Although there is perhaps an artistic element to rigid contact lens fitting, the solid literature base underpinning the field of rigid contact lenses revealed here belies the notion that rigid lenses fitting is more of an art than a science.
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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 | 0.016 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".