Evaluation of systematic reviews of treatment or prevention interventions
Bibliographic record
Abstract
In a previous article in this series we explained how the critical appraisal of research is an essential step in evidence-based health care because most published research is too poor in quality to be applied to clinical practice.1 Critical appraisal is made easier through the use of quality checklists that can help you to appraise research studies systematically and efficiently. The 3 basic appraisal questions are the same whether the clinical question is about treatment, diagnosis, prognosis, or causation: The first 2 articles in the EBN users' guide series focused on critical appraisal of primary studies of treatment or prevention.1,2 This guide will deal with critical appraisal of systematic reviews, beginning with a clinical scenario and applying the appraisal questions to the review by Glazener and Evans on the effectiveness of alarm interventions for nocturnal enuresis in children.4 ### How to critically appraise review articles Are the results of this systematic review valid? What were the results? Will the results help me in caring for my patients?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.399 | 0.732 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.021 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".