An analysis for status of lung cancer translational research based on science citation index database
Bibliographic record
Abstract
Objective To investigate the status of translational research in lung cancer in the past decade. Methods The papers of lung cancer translational research from the Web of Science database were analyzed by bibliometrics methods. Results A total of 9 523 papers of translational medicine related lung cancer were indexed by science citation index( SCI) from 2004 to 2014. The number of papers showed an upward trend. The top 10 countries were the United States,England,Germany,China,Canada,Italy,Japan,France,Netherlands and Australia. The top 20 research institutions were in the United States except Canada Toronto University. A total of 497 journals posted more than 5 articles. Translational Research,Stem Cells Translational Medicine,PLo S One,Cts Clinical and Translational Science and Science Translational Medicine ranked the top 5 journals which issued a volume of more than 100 articles. Web of Science category showed that medicine research experimental ranked the first with accounting for 12. 202%,oncology ranked second with accounting for 9. 010%. Conclusion Basic research is still the focus of translational medicine,support for clinical research project must be strengthened,we truly achieve translational medicine applied in clinical treatment of lung cancer.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.068 | 0.091 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".