AHRQ series on complex intervention systematic reviews—paper 6: PRISMA-CI extension statement and checklist
Bibliographic record
Abstract
BACKGROUND: Complex interventions are widely used in health systems, public health, education, and communities and are increasingly the subject of systematic reviews. Oversimplification and inconsistencies in reporting about complex interventions can limit the usability of review findings. RATIONALE: Although guidance exists to ensure that reports of individual studies and systematic reviews adhere to accepted scientific standards, their design-specific focus leaves important reporting gaps relative to complex interventions in health care. This paper provides a stand-alone extension to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting tool for complex interventions-PRISMA-CI-to help authors, publishers, and readers understand and apply to systematic reviews of complex interventions. DISCUSSION: PRISMA-CI development followed the Enhancing the QUAlity and Transparency Of health Research Network guidance for extensions and focused on adding or modifying only essential items that are truly unique to complex interventions and are not covered by broader interpretation of current PRISMA guidance. PRISMA-CI provides an important structure and guidance for systematic reviews and meta-analyses for the highly prevalent and dynamic field of complex interventions.
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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 | 0.472 | 0.658 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".