Prostate Cancer Research: A Bibliometric Study of India and Iran
Bibliographic record
Abstract
The study aims to provide an insight into the global research productivity in prostate cancer with an in-depth analysis of the growth & development of India and Iran. The study focuses on the authorship collaborative patterns among Indian and Iranian medical scientists as well. \nThe study was commenced with the selection of terms on “Prostate cancer”. Three terms�Prostate Cancer, Prostate Neoplasm, and Prostatic Neoplasm were selected from the \nMedical Subject Headings (MeSH) to retrieve the data from the Web of Science (WoS). The Boolean Operator “OR” was executed to retrieve the records. The data related to prostate cancer research from 1989-2017 was retrieved and downloaded in the excel file. Later, Microsoft Excel software was used to analyze the data. Three important means- annual growth rate (AGR), relative growth rate (RGR), and Doubling Time (DT) have been used to trace the development of literature from 1989 to 2017. Further, authorship patterns were analyzed using the authorship collaboration and collaborative coefficient methods. The \nannual growth rate is slow in the onset as compared to the later years, which is a positive sign of the improvement in the research productivity of India and Iran while as relative growth rate shows a decrease, doubling time shows an increasing trend in both nations towards the end of 2017. Authors prefer to work in collaboration rather than individually as is evident from the values of Collaboration Coefficient and Degree of Collaboration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.072 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".