Topical Review: Bibliometric Analysis of the Emerging Field of Myopia Management
Bibliographic record
Abstract
SIGNIFICANCE: Identification of the most impactful articles, authors, institutions, countries, and journals in myopia management provides a useful baseline reference for clinicians, researchers, and funding agencies in respect of this emerging field.This work aims to assemble publication metrics for myopia management to identify the most impactful articles, authors, institutions, countries, and journals in this emerging field of research. A search of the titles of articles was undertaken on the Scopus database to identify myopia management-related articles. The 25 most highly cited articles were determined from the total list of 1064 articles found. Rank-order lists by count were assembled for the top 25 in each of four categories: authors, institutions, countries, and journals. A subject-specific myopia management-related h-index (hMM-index) was derived for the entire field, in addition to each of the four categories, to serve as measures of impact in the field. Top 15 lists were generated for each category ranked by hMM-index and tabulated for consideration. An article by Christine Wildsoet and colleagues, describing choroidal and scleral mechanisms of compensation for spectacle lenses in chicks, has generated the most citations (412); Earl Smith is the most impactful author (hMM = 19); the University of Houston produces the most impactful articles (hMM = 31); the United States is the most highly ranked country (hMM = 60); and Optometry and Vision Science is the most impactful journal. Although still in its infancy, myopia management is a topic of emerging interest in the clinical and scientific ophthalmic literature. Impactful authors, institutions, countries, and journals are identified. Optometry is revealed as the leading profession in relation to the publication of myopia management-related articles.
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 | 0.019 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.173 | 0.216 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".