AHRQ series on complex intervention systematic reviews—paper 7: PRISMA-CI elaboration and explanation
Bibliographic record
Abstract
BACKGROUND: Complex interventions are widely used in health care, public health, education, criminology, social work, business, and welfare. They have increasingly become the subject of systematic reviews and are challenging to effectively report. The Complex Interventions Methods Workgroup developed an extension to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Complex Interventions (PRISMA-CI). RATIONALE: Following the EQUATOR Network guidance for Preferred Reporting Items for Systematic Reviews and Meta-Analysis extensions, this Explanation and Elaboration (EE) document accompanies the PRISMA-CI checklist to promote consistency in reporting of systematic reviews of complex interventions. DISCUSSIONS: The EE document explains the meaning and rationale for each unique PRISMA-CI checklist item and provides examples to assist systematic review authors in operationalizing PRISMA-CI guidance. The Complex Interventions Workgroup developed PRISMA-CI as an important start toward increased consistency in reporting of systematic reviews of complex interventions. Because the field is rapidly expanding, the Complex Interventions Methods Workgroup plans to re-evaluate periodically for the need to add increasing specificity and examples as the field matures.
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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 | 0.500 | 0.737 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.068 | 0.030 |
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".