Publication Trends in Rheumatology Systematic Reviews and Randomized Clinical Trials, 1995–2017
Bibliographic record
Abstract
To the Editor: The growth of systematic reviews and metaanalyses (SRMA) has outpaced the growth of randomized clinical trials (RCT) in many medicine subspecialties1. This may reflect technological advances in SRMA production, fewer barriers to publish, or academic pressure to produce citations2. The value of disproportionate SRMA growth has been brought into question3, but the nature of RCT growth has undergone less scrutiny. In rheumatology, nearly 4 in 5 RCT receive pharmaceutical industry funding4, which could influence the relative proportion of early-stage efficacy studies as opposed to postmarketing safety studies. In this letter we describe the relative growth of rheumatology RCT and SRMA, as well as the phase of clinical trials over time, neither of which have been previously assessed in the field of rheumatology. We conducted a cross-sectional study using the R package RISmed (R Foundation for Statistical Computing), which extracted bibliographic content from the database PubMed. The inclusion period began on … Address correspondence to Dr. M.S. Putman, Northwestern University, Department of Medicine 251 E Huron St. #1400, Chicago, IL 60611, USA. Email: msputman{at}gmail.com.
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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 | 0.060 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".