Implications of applying methodological shortcuts to expedite systematic reviews: three case studies using systematic reviews from agri‐food public health
Bibliographic record
Abstract
BACKGROUND: The rapid review is an approach to synthesizing research evidence when a shorter timeframe is required. The implications of what is lost in terms of rigour, increased bias and accuracy when conducting a rapid review have not yet been elucidated. METHODS: We assessed the potential implications of methodological shortcuts on the outcomes of three completed systematic reviews addressing agri-food public health topics. For each review, shortcuts were applied individually to assess the impact on the number of relevant studies included and whether omitted studies affected the direction, magnitude or precision of summary estimates from meta-analyses. RESULTS: In most instances, the shortcuts resulted in at least one relevant study being omitted from the review. The omission of studies affected 39 of 143 possible meta-analyses, of which 14 were no longer possible because of insufficient studies (<2). When meta-analysis was possible, the omission of studies generally resulted in less precise pooled estimates (i.e. wider confidence intervals) that did not differ in direction from the original estimate. CONCLUSIONS: The three case studies demonstrated the risk of missing relevant literature and its impact on summary estimates when methodological shortcuts are applied in rapid reviews. © 2016 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd.
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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 | 0.779 | 0.914 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".