The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions
Bibliographic record
Abstract
BACKGROUND: Overviews of reviews (overviews) compile information from multiple systematic reviews (SRs) to provide a single synthesis of relevant evidence for decision-making. Overviews may identify multiple SRs that examine the same intervention for the same condition and include some, but not all, of the same primary studies. There is currently limited guidance on whether and how to include these overlapping SRs in overviews. Our objectives were to assess how different inclusion decisions in overviews of healthcare interventions affect their comprehensiveness and results, and document challenges encountered when making different inclusion decisions in overviews. METHODS: We used five inclusion decisions to conduct overviews across seven topic areas, resulting in 35 overviews. The inclusion decisions were (1) include all Cochrane and non-Cochrane SRs, (2) include only Cochrane SRs, or consider all Cochrane and non-Cochrane SRs but include only non-overlapping SRs, and in the case of overlapping SRs, select (3) the Cochrane SR, (4) the most recent SR (by publication or search date), or (5) the highest quality SR (assessed using AMSTAR). For each topic area and inclusion scenario, we documented the amount of outcome data lost and changed and the challenges involved. RESULTS: When conducting overviews, including only Cochrane SRs, instead of all SRs, often led to loss/change of outcome data (median 31% of outcomes lost/changed; range 0-100%). Considering all Cochrane and non-Cochrane SRs but including only non-overlapping SRs and selecting the Cochrane SR for groups of overlapping SRs (instead of the most recent or highest quality SRs) allowed the most outcome data to be recaptured (median 42% of lost/changed outcome recaptured; range 28-86%). Across all inclusion scenarios, challenges were encountered when extracting data from overlapping SRs. CONCLUSIONS: Overlapping SRs present a methodological challenge for overview authors. This study demonstrates that different inclusion decisions affect the comprehensiveness and results of overviews in different ways, depending in part on whether Cochrane SRs examine all intervention comparisons relevant to the overview. Study results were used to develop an evidence-based decision tool that provides practical guidance for overview authors.
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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 | 0.789 | 0.944 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".