Reporting and interpretation of effects in nutritional and environmental epidemiology: a methods study
Bibliographic record
Abstract
Abstract Background The presentation of absolute effects, in addition to relative effects, is critical to the optimal interpretation of effect estimates. Failure to present and interpret absolute effects may obscure the magnitude of the effect of an intervention or exposure and mislead evidence users. Objective In this study, we estimate the proportion of systematic reviews and meta-analyses (SRMAs) addressing the health effects of nutritional and environmental exposures that report absolute effects. Methods We searched MEDLINE and EMBASE from 2019 through 2021 for SRMAs addressing the health effects of nutritional and environmental exposures and patient-important health outcomes. We included a sample of 200 SRMAs. Pairs of reviewers, working independently and in duplicate, reviewed search records for eligibility and collected data from SRMAs. Results More than two-thirds (153/200; 76.5%) of eligible systematic reviews reported on one or more dichotomous outcomes that could be translated to absolute effects. Only a handful of these reviews (8/153; 5.2%), however, reported absolute effects. A similar proportion of reviews published in high-impact journals and in other journals reported absolute effects (4/131; 3.1% vs. 4/69; 5.9%). Among reviews that reported absolute effects, six reviews (6/8; 75%) reported absolute risk differences as fractions (e.g., 2 fewer cases per 1,000 people) and two reviews (2/8; 25%) presented the number of cases prevented by modifying the exposure (e.g., 2,000 cases prevented in United States annually). Conclusion Reviews addressing the effects of nutritional and environmental exposures on health outcomes rarely report absolute effects, which precludes effective interpretation of magnitudes of effects and their importance. We present guidance for review authors, editors, peer reviewers, and evidence users to calculate and interpret absolute effects.
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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 | 0.544 | 0.805 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".