Analysis of Iranian Breast Cancer Research: A Scientometric Study
Bibliographic record
Abstract
Background and Aim: Scientometric studies are one of the most effective methods of scientific evaluation in databases. The aim of this study was to investigate Breast cancer in Iran from 2000-2016. Materials and Methods: This study has an applied approach and was conducted using scientometric indices. The st tistical population was the indexed articles related to Breast cancer between the years 2000 and 2016 by Iranian researchers at the Science Web site. Results: During the period 2000-2016, researchers have published 2198 articles on Breast cancer that indicate a steady and steady increase in the number of studies conducted in this area. The results of the study showed that Qaderi is the most prolific researcher in the field of Breast cancer in terms of the number of articles in Iran, Ebrahimi and Montazeri are in the second and third positions respectively. The highest H-index belongs to Montazeri, Qaderi and Abraham, respectively. Researchers in the field of Breast cancer have collaborated with researchers from 65 countries and more with the United States and Canada. The most co-operation has been between researchers in Tehran and Tabriz. The analysis of the keywords used in Breast cancer research in the form of supragloss showed that Iran, Apoptosis and Polymorphism were the most frequent keywords in the studied works. Conclusion: The upward trend in Breast cancer research indicates the growing importance of this area in Iran. Given the global growth of Breast cancer research and the importance of international research participation, Iranianresearchers should more and more engage in scientific collaboration with their counterparts abroad.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.060 | 0.090 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".