Cough as a symptom and a disease entity: scientometric analysis and density-equalizing calculations.
Bibliographic record
Abstract
BACKGROUND: Cough is a prominent symptom of many allergic diseases and a major health burden but there is little information available on the current state of research in this area. OBJECTIVES: To analyze long-term developments in cough research and recent trends. METHODS: We searched the Thomson Reuters Web of Science databases for cough-related items published between 1900 and 2007 and analyzed the results using scientometric methods and density-equalizing calculations. RESULTS: We found 12 960 cough-related publications from 132 countries for the period studied. The most productive country was the United States of America (USA), followed by the United Kingdom (UK), France, Japan, Canada, and Germany. These 12 960 published items were cited 165 868 times. The average number of citations per item increased from 1976 to 1992, with peaks in 1977, 1979, 1981, 1984, 1989 and 1992. Each of these years was followed by a decrease in citation numbers. Bilateral and multilateral cooperation analysis using the radar chart technique showed a progressive increase in international co-authorship starting at the beginning of the 1990s, with a leading role by the USA and the UK. CONCLUSION: We detected a marked increased in cough-related research starting in the 1990s. While the majority of data originates from the US, other countries have taken a leading position in terms of research quality (number of citations per item).
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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 | 0.033 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.100 | 0.111 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".