Dealing with predatory journal articles captured in systematic reviews
Bibliographic record
Abstract
BACKGROUND: Systematic reviews appraise and synthesize the results from a body of literature. In healthcare, systematic reviews are also used to develop clinical practice guidelines. An increasingly common concern among systematic reviews is that they may unknowingly capture studies published in "predatory" journals and that these studies will be included in summary estimates and impact results, guidelines, and ultimately, clinical care. FINDINGS: There is currently no agreed-upon guidance that exists for how best to manage articles from predatory journals that meet the inclusion criteria for a systematic review. We describe a set of actions that authors of systematic reviews can consider when handling articles published in predatory journals: (1) detail methods for addressing predatory journal articles a priori in a study protocol, (2) determine whether included studies are published in open access journals and if they are listed in the directory of open access journals, and (3) conduct a sensitivity analysis with predatory papers excluded from the synthesis. CONCLUSION: Encountering eligible articles published in presumed predatory journals when conducting a review is an increasingly common threat. Developing appropriate methods to account for eligible research published in predatory journals is needed to decrease the potential negative impact of predatory journals on healthcare.
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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 | MetaresearchScholarly communication Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.448 | 0.865 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".