Prostate cancer research in India: A scientometric analysis of publications output during 2004-13
Bibliographic record
Abstract
This review article examines 1,368 publications on prostate cancer in India, as covered in Scopus database during 2004-13, experiencing an annual average growth rate of 18.77% and citation impact of 5.23. The world prostate cancer output (89,994 publications) came from several countries, of which the top 15 (United States, United Kingdom, Germany, Canada, Italy, Japan, and China) accounts for 94.80% share of the global output during 2004-13. India's global publication share was 1.52% and hold 14 th rank in global publication output during 2004-13. The Indian prostate cancer output came from several organizations and authors, of which the top 20 and 19 contributed 41.81% and 24.05% share, respectively, during 2004-13. India's international collaborative share in prostate cancer was 23.39%, which decreased from 24.42% to 22.98% from 2004-08 to 2009-13. Medicine accounted for the largest share (59.50%) of output in prostate cancer followed by biochemistry, genetics and molecular biology (40.13%), pharmacology, toxicology & pharmaceutics (27.63%), chemistry (8.55%), agricultural and biological sciences (4.31% share), and immunology and microbiology (2.70% share) during 2004-13. Diagnosis, screening, chemotherapy, radiotherapy, pathology and prognosis together account for 60.24% publications share among treatments methods used in Indian prostate cancer research during 2004-13. Only Delhi, Maharashtra, Uttar Pradesh and Tamil Nadu together contributed 57.82% share in Indian publications output in prostate cancer during 2004-13. The authors stressed the need for developing national policy for prostate cancer which should take care of screening for detection and diagnosis, management and treatment options of the prostate cancer patients in India.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.057 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".