Eye neoplasms: a bibliometric analysis from 1966 to 2012
Bibliographic record
Abstract
Abstract Purpose To calculate the growth rate of biomedical literature on eye neoplasms and to assess key journals, authors, and country affiliations. Methods PubMed was used to search for papers published from 1966 to 2012. Total number of articles per year was fitted to a linear equation as well as an exponential curve. To identify the core journals and predict the number of journals containing articles related to eye neoplasms, Bradford's law was applied. The mean number of publications per year and per author were calculated. For each country, the Gross Domestic Product (GDP) index (publications per 1 billion US dollars of GPD) and the population index (publications per million inhabitants) were calculated. Results A total of 27 943 references were retrieved. The growth in the number of publications showed a linear increase with a yearly average growth rate of 2.08%. Using Bradford's law, 17 core journals were identified. Only 9 authors published more than 5 papers per year. The United States was by far the predominant country in number of publications, followed by Germany and the United Kingdom. However, population and Gross Domestic Product indexes showed that absolute production did not reflect the production per capita nor the economic efficiency Conclusion This bibliometric study provides data contributing to a better understanding of the eye neoplasm research field.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.156 | 0.184 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".