Can We Systematically Review Studies That Evaluate Complex Interventions?
Bibliographic record
Abstract
BACKGROUND TO THE DEBATE: The UK Medical Research Council defines complex interventions as those comprising "a number of separate elements which seem essential to the proper functioning of the interventions although the 'active ingredient' of the intervention that is effective is difficult to specify." A typical example is specialist care on a stroke unit, which involves a wide range of health professionals delivering a variety of treatments. Michelle Campbell and colleagues have argued that there are "specific difficulties in defining, developing, documenting, and reproducing complex interventions that are subject to more variation than a drug". These difficulties are one of the reasons why it is challenging for researchers to systematically review complex interventions and synthesize data from separate studies. This PLoS Medicine Debate considers the challenges facing systematic reviewers and suggests several ways of addressing them.
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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 | 0.429 | 0.829 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.012 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.019 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".