The problems with systematic reviews: a living systematic review
Bibliographic record
Abstract
OBJECTIVES: Systematic reviews and meta-analyses are proliferating as they are an important building block to inform evidence-based guidelines and decision-making. Enforcement of best practice in clinical trials is firmly on the research agenda of good clinical practice, but there is less clarity as to how evidence syntheses that combine these studies can be influenced by bad practice. Our aim was to conduct a living systematic review of articles that highlight flaws in published systematic reviews to formally document and understand these problems. STUDY DESIGN AND SETTING: We conducted a comprehensive assessment of all literature examining problems, which relate to published systematic reviews. RESULTS: The first iteration of our living systematic review (https://systematicreviewlution.com/) has found 485 articles documenting 67 discrete problems relating to the conduct and reporting of systematic reviews which can potentially jeopardize their reliability or validity. CONCLUSION: Many hundreds of articles highlight that there are many flaws in the conduct, methods, and reporting of published systematic reviews, despite the existence and frequent application of guidelines. Considering the pivotal role that systematic reviews have in medical decision-making due to having apparently transparent, objective, and replicable processes, a failure to appreciate and regulate problems with these highly cited research designs is a threat to credible science.
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 | 0.755 | 0.893 |
| Meta-epidemiology (narrow) | 0.004 | 0.011 |
| Meta-epidemiology (broad) | 0.018 | 0.011 |
| Bibliometrics | 0.038 | 0.033 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".