The landscape of systematic reviews in urology (1998 to 2015): an assessment of methodological quality
Bibliographic record
Abstract
OBJECTIVES: To assess the quality of published systematic reviews in the urology literature (an extension of our previously reported work), as high-quality systematic reviews play a paramount role in informing evidence-based clinical practice. MATERIALS AND METHODS: Our focus was on systematic reviews in the urology literature that incorporated questions of prevention and therapy. To identify such reviews published during a 36-month period (2013-2015), we systematically searched PubMed and hand-searched the table of contents of four major urology journals. Two reviewers independently assessed the methodological quality of those reviews, using the 11-point 'Assessment of Multiple Systematic Reviews' (AMSTAR) instrument. We performed protocol-driven analyses of the data from our present study's 36-month period alone, as well as in aggregate with the data from our previously reported work's study periods (2009-2012 and 1998-2008). RESULTS: In our literature search of the 36-month period (2013-2015), we initially identified 490 possibly relevant reviews, of which 125 met our inclusion criteria. The most common topic of reviews for the 2013-2015 period was oncology (51.2%; n = 64), followed by voiding dysfunction (21.6%; n = 27). The mean [standard deviation (SD)] AMSTAR score in the 2013-2015 period (n = 125) was 4.8 (2.4); 2009-2012 (n = 113), 5.4 (2.3); and 1998-2008 (n = 57), 4.8 (2.0) (P = 0.127). In the 2013-2015 period, the mean (SD) AMSTAR score for the BJU International (n = 25) was 5.6 (2.9); for The Journal of Urology (n = 20), 5.1 (2.6); for European Urology (n = 60), 4.5 (2.2); and for Urology (n = 20), 4.4 (2.2) (P = 0.106). CONCLUSIONS: The number of systematic reviews published in the urology literature has exponentially increased, year by year, but their methodological quality has stagnated. To enhance the validity and impact of systematic reviews, all authors and editors must apply established methodological standards.
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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 | 0.624 | 0.842 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.073 | 0.080 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".