Bibliometric analysis of the keratoconus literature
Bibliographic record
Abstract
Clinical Relevance Clinicians, researchers funding agencies and indeed the general public can benefit from knowledge of the most highly cited papers and most impactful authors, institutions, countries and journals in the field of keratoconus.Background Bibliometrics relating to the keratoconus literature were derived to enable identification of the most impactful papers published, as well as the leading authors, institutions, countries and journals.Methods A search was undertaken of the titles of papers on the Scopus database to identify keratoconus-related articles. The 20 most highly cited papers were determined from the total list of 4,419 papers found. Rank-order lists by count were assembled for the ‘top 20ʹ in each of four categories: authors, institutions, countries and journals. A subject-specific keratoconus-related h-index (hKC-index) was derived for each constituent of each category to serve as a measure of impact in the field. The top 10 constituents of each category were ranked by hKC-index and tabulated for consideration.Results The hKC-index of the keratoconus field is 125. The 4,419 papers have been cited a total of 98,010 times, and 18.5% of these papers have never been cited. The most highly cited paper is a general review of keratoconus by Yaron Rabinowitz, who is also the most impactful author in the field (hKC = 31). The Cedars Sinai Medical Center in the United States produces the most impactful keratoconus-related papers (hKC = 36), and the United States is the most impactful country (hKC = 91). The Journal of Cataract and Refractive Surgery is the most impactful journal (hKC = 55).Conclusion Keratoconus is a topic of high interest in the clinical and scientific literature. Highly cited papers and impactful authors, institutions, countries and journals are identified.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.012 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.235 | 0.237 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".