Global Asthma Research with Special Reference to India: A Scientometric Assessment of Publication Output during 2007-16
Bibliographic record
Abstract
The paper examines 2094 Indian publications on asthma research, as covered in Scopus database during 2007-16, registering an annual average growth rate of 12.14%, global share of 2.72%, qualitative citation impact averaged to 10.857citations per paper and international collaborative publication share of 13.51%. The top 12 most productive countries individually contributed global share from 2.72% to31.27%, with largest global publication share coming from USA (31.27%), followed by U.K. (10.45%), Germany (5.60%), Canada (5.44%), etc. Together, the 12 most productive countries accounted for 83.80% share of global publication output during 2007-16.Medicine, among subjects, accounted for the highest publications share (61.32%), followed by pharmacology, toxicology & pharmaceutics (35.05%) biochemistry, genetics & molecular biology (20.25%), immunology & microbiology (7.78%), agricultural & biological sciences (3.44%) and chemistry (2.88%) and during 2007-16. Among different type of asthma, allergic asthma contributed the highest number of publications, followed by bronchial asthma, atopic asthma, occupational asthma and seasonal asthma, etc. during 2007-16.The top 15most productive organizations and authors together contributed 33.48% and 19.96% respectively as their share of global publication output and 43.88% and 36.93% respectively as their share of global citation output during 2007-16.
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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 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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".