Applying an Author-Weighted Scheme to Identify the Most Influential Countries in Research Achievements on Skin Cancer: Observational Study
Why this work is in the frame
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Bibliographic record
Abstract
Background Skin cancers are caused by the development of abnormal cells that can invade or spread to other parts of the body. The countries whose authors contribute the most amount of articles on skin cancer to academia is still unknown. Objective The objectives of this study are to apply an author-weighted scheme (AWS) to quantify the credits for coauthors on an article byline and allocate the author weights to the country-level credits in articles. Methods On July 20, 2019, we obtained 16,804 abstracts published since 1938, based on a keyword search of “skin cancer” in PubMed. The author names, countries/areas, and journals were recorded. International author collaborations on skin cancer were analyzed based on country-level credits in articles. We aimed to do the following: (1) present country distribution for the first authors and the most popular journals, (2) show choropleth maps to highlight the most influential countries, and (3) draw scatter plots based on the Kano model to characterize the features of country-level research achievements. We programmed Excel Visual Basic for Applications (Microsoft Corp) routines to extract data from PubMed. Google Maps was used to display graphical representations. Results Our results suggest that researchers in the United States have published most frequently, accounting for 30.37% (5103), while Germany accounts for 7.34% (1234), followed by Australia (997, 5.93%). The top three continents for the proportion of published articles are North America, Europe, and Asia, accounting for 32.29%, 31.71%, and 10.41%, respectively. Conclusions This study offers an objective picture of the representativeness and evolution of international research on the topic of skin cancer. The research approaches used here have the potential to be applied to other areas besides skin cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it