Lung Cancer in India: A Scientometric Study of Publications during 2005–14
Bibliographic record
Abstract
This paper examines 3,653 Indian publications on lung cancer research, as covered in Scopus database during 2005-14, experiencing an annual average growth rate of 18.81% and citation impact of 4.20.The world lung cancer output (169,352 publications) came from several countries, of which the top 15 most productive countries (United States, China, Germany, Japan, United Kingdom, Italy, France, Canada, and South Korea) accounted for 93.17% share of the global output during 2005-14.India's global publication share was 2.16% and holds 12th rank in the global output during 2005-14.India's share of international collaborative publications on lung cancer was 17.79% during 2005-14, which decreased from 19.89 to 17.06% from 2005-09 to 2010-14.Breast cancer in the field of medicine accounted for the largest share (63.62%) of output, followed by biochemistry, genetics and molecular biology (28.77%); pharmacology, toxicology, and pharmaceutics (23.87%); chemistry (9.31%); agricultural and biological sciences (3.26%); and immunology and microbiology (2.23%) during 2005-14.Diagnosis, chemotherapy, surgery, and radiotherapy among treatments methods together accounted for a share of 61.20% publications in Indian lung cancer research during 2005-14.Among the different states, Maharashtra, Delhi, Karnataka, Chandigarh, and Telangana together account for 53.41% share during 2005-14.In India's cumulative lung cancer publications output during 2005-14, the most productive 14 Indian organizations, 15 authors, and 15 journals together contributed to 33.71, 11.27, and 20.23% share, respectively.The 31 high-cited papers in lung cancer research registered an average citation per paper of 294.74.Of the 31 high-cited papers (19 articles and 12 reviews), 7 were single institution, 3 national collaborative, and 21 international collaborative papers.The 31 high-cited papers have appeared in 23 journals.In light of this, the authors suggest the need to develop a National Cancer Prevention Policy, which should make specific recommendations for national action by governments and non-government organizations, including programs and strategies, to reduce the incidence of specific preventable cancer types.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.105 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".