Bibliometric analysis of research papers on mutant prevention concentration
Bibliographic record
Abstract
Objective: To analyze the recent advance on mutant prevention concentration (MPC) over the past 11 years with bibliometric approach for advanced research of MPC. Methods: Literatures were searched from Medline, Embase, Science Citation Index, China Hospital Knowledge database and VIP Chinese Journal database from Jan. 1999 to Mar. 2010. EndNote 2.0 was used to sort these articles and the bibliometric analysis was carried out in respect of published years, first authors, published institutions, countries and contents, etc. Results: A total of 270 articles were collected in our research, including 203 English articles and 67 Chinese papers. There were 156 original articles, 63 reviews and 43 conference articles. The first article of MPC was reported in 1999. The number of articles about MPC published in 2004 was the most. Canadian royal university hospital ranked the f irst place in all of institutions in terms of published articles relating to MPC, while the Chinese PLA General Hospital ranked the first place in China. From the aspect of content, most of the articles focused on the in vitro and animal study. One article reported the clinical validation of MPC hypothesis in tuberculosis patient. Most of researches focused on Staphylococci, Streptococcus pneumoniae and Pseudomonas aeruginosa. Conclusion: Many researchers pay more attention to MPC theory and the research will be studied deeper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.239 | 0.254 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".