Methodological Quality of Systematic Reviews for Questions of Therapy and Prevention Published in the Urological Literature (2016–2021) Fails to Improve
Bibliographic record
Abstract
ObjectivesPrior studies have suggested that few systematic reviews (SRs) published in the urological literature provide reliable evidence. We performed this study to provide a longitudinal analysis of the methodological quality of SRs published in 5 major urology journals over a 6-year period (2016–2021).MethodsAs an extension of a prior study with a written a priori protocol, we systematically searched and analyzed all SRs related to questions of therapy or prevention published in the 5 major urology journals. Three independent reviewers working in pairs selected eligible studies and abstracted the data in duplicate. We used the updated Assessment of Multiple Systematic Reviews (AMSTAR-2) instrument to assess SR quality. We performed pre-planned statistical hypothesis testing by time period and journal of publication in SPSS Version 27.0.ResultsOur updated search (2019–2021) identified 563 references of which 114 ultimately met inclusion criteria, which we added to the database of the prior 144 studies (2016–2018). Overall, among 258 SRs, only 6 (2.3%) and 9 SRs (3.5%), achieved a “high” (no critical weakness; up to one non-critical weakness) or “moderate” (no critical weakness; more than one non-critical weakness) confidence rating, respectively. Most SRs published had very low confidence rating (195; 75.6%). The proportion of studies with a high or moderate rating (6.1% versus 4.9%; P = 0.481) did not increase over time.ConclusionsMost SRs published in the urological literature continue to have serious methodological limitations and should not be relied upon. There is a critical need for greater awareness for established methodological standards.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.549 | 0.818 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".