Mouth Cancer Research: A Quantitative Analysis of World Publications, 2003-12
Bibliographic record
Abstract
The paper presents an analysis of 37049 world papers in mouth cancer, indexed in Scopus database during 2003-12, experiencing an annual average growth rate of 5.15 % and citation impact of 9.72. The 15 most productive countries account for 88.14 % share in world output, with largest share (26.79 %) coming from USA, followed by Japan (9.31 %), UK (7.58 %), Germany (5.82 %), Italy (5.60 %), China (4.98 %), India (4.94 %), etc., during 2003-12. Eight out of 20 countries have achieved relative citation index above 1–France (1.74), Australia (1.58), Netherlands (1.55), Canada (1.43), USA (1.33k), Germany (1.21), UK (1.16), Italy (1.06), and Spain (1.05) during 2003-12. Medicine contributed the largest share (82.72 %) among subjects, followed by biochemistry, genetics & molecular biology (29.33 %), dentistry (14.36 %), pharmacology, toxicology & pharmaceutics (8.36 %), immunology & microbiology (1.90 %), etc during 2003-12. In cancer site, tongue, salivary glands and oropharynx contributed the largest share of 12.04 %, 10.02 % and 8.44 % respectively during 2003-12. Squamous cell carcinoma contributed the largest share of 27.20 % among types of mouth cancer research, followed by lymphomas (12.72 %), salivary gland carcinoma (10.02 %), and melanoma (3.36 %) etc during 2003-12. Surgery contributed the largest share (15.77 %) among treatment methods used, followed by chemotherapy (14.99 %), diagnosis (13.20 %), radiotherapy (12.86 %), pathology (12.48 %), etc. during 2003-12. Among several organisations, authors and journals, the top 20 contributed 14.1 %, 4.27 %, and 23.16 % share respectively during 2003-12. http://dx.doi.org/ 10.14429/djlit.34.7341
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.066 | 0.128 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".