SUPPORT Tools for evidence-informed health Policymaking (STP) 8: Deciding how much confidence to place in a systematic review
Bibliographic record
Abstract
This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. The reliability of systematic reviews of the effects of health interventions is variable. Consequently, policymakers and others need to assess how much confidence can be placed in such evidence. The use of systematic and transparent processes to determine such decisions can help to prevent the introduction of errors and bias in these judgements. In this article, we suggest five questions that can be considered when deciding how much confidence to place in the findings of a systematic review of the effects of an intervention. These are: 1. Did the review explicitly address an appropriate policy or management question? 2. Were appropriate criteria used when considering studies for the review? 3. Was the search for relevant studies detailed and reasonably comprehensive? 4. Were assessments of the studies' relevance to the review topic and of their risk of bias reproducible? 5. Were the results similar from study to study?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".