Small studies in systematic reviews: To include or not to include?
Bibliographic record
Abstract
Background: COVID-19 provided a real challenge for evidence synthesis due to the rapid growth of evidence. We aim to assess the impact of including all studies versus including larger studies only in systematic reviews when there is plethora of evidence. We use a case study of COVID-19 and chronic kidney disease (CKD). Methods: The review team conducted a systematic review of multiple databases. The review assessed the effect of CKD on mortality in patients with COVID-19. We performed a sensitivity analysis to assess the effect of study size on the robustness of the results based on cutoffs of 500, 1000 and 2000 patients. Results: We included 75 studies. Out of which there were 40 studies with a sample size of >2,000 patients, seven studies with 1,000-2,000 patients, 11 studies with 500-1,000 patients, and 17 studies with <500 patients. CKD increased the risk of mortality with a pooled hazard ratio (HR) 1.57 (95% confidence interval (CI) 1.42 - 1.73), odds ratio (OR) 1.86 (95%CI 1.64 - 2.11), and risk ratio (RR) 1.74 (95%CI 1.13 - 2.69). Across the three cutoffs, excluding the smaller studies resulted in no statistical significance difference in the results with an overlapping confidence interval. Conclusions: These findings suggested that, in prognosis reviews, it could be acceptable to limit meta-analyses to larger studies when there is abundance of evidence. Specific thresholds to determine which studies are considered large will depend on the context, clinical setting and number of studies and participants included in the review and meta-analysis.
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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 | 0.389 | 0.772 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.018 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".