Trends in matrix metalloproteinase research from 1986–2007: a bibliometric study
Bibliographic record
Abstract
Using the SCI-expanded database, this study provides a quantitative description of the development of the research involving matrix metalloproteinase (MMP) over a period of 20 years. From 1986 to 2007 the scientific literature related to MMP increased sevenfold (397 papers in 1986-1987 and 2834 in 2006-2007). The number of countries participating in MMP-related research doubled during this period (33 in 1986-1987 to 67 in 2006-2007), and the USA continually remained the leader. Several industrialized nations (Japan, Germany, UK, Canada, and France) also continuously played important roles, with some emerging Asian countries joining the top 10 most productive countries in 2006-2007: China (ranked 5th), South Korea (6th), and Taiwan (10th). The MMP-related literature was distributed among a continuously growing number of journals (188 in 1986-1987, 527 in 1996-1997, and 913 in 2006-2007) and The Journal of Biological Chemistry remained the most prolific throughout the entire period. The development of the research involving MMPs during the past two decades was also characterized by a progressive transfer of interest from basic research to clinical medicine; cell biology and pharmacology were important routes of investigation generally pursued by researchers. Journals dedicated to oncology have progressively risen to the top 8 most prolific journals during the 20 year period analyzed.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.090 | 0.202 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".