Critical appraisal of nonrandomized studies—A review of recommended and commonly used tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: When randomized controlled trial data are limited or unavailable, or to supplement randomized controlled trial evidence, health technology assessment (HTA) agencies may rely on systematic reviews of nonrandomized studies (NRSs) for evidence of the effectiveness of health care interventions. NRS designs may introduce considerable bias into systematic reviews, and several methodologies by which to evaluate this risk of bias are available. This study aimed to identify tools commonly used to assess bias in NRS and determine those recommended by HTA bodies. METHODS: Appraisal tools used in NRS were identified through a targeted search of systematic reviews (January 2013-March 2017; MEDLINE and EMBASE [OVID SP]). Recommendations for the critical appraisal of NRS by expert review groups and HTA bodies were reviewed. RESULTS: From the 686 studies included in the narrative synthesis, 48 critical appraisal tools were identified. Commonly used tools included the Newcastle-Ottawa Scale, the methodological index for NRS, and bespoke appraisal tools. Neither the Cochrane Handbook nor the Centre for Reviews and Dissemination recommends a particular instrument for the assessment of risk of bias in NRS, although Cochrane has recently developed their own NRS critical appraisal tool. Among HTA bodies, only the Canadian Agency for Drugs and Technologies in Health recommends use of a specific critical appraisal tool-SIGN 50 (for cohort or case-control studies). Several criteria including reporting, external validity, confounding, and power were examined. CONCLUSION: There is no consensus between HTA groups on the preferred appraisal tool. Reviewers should select from a suite of tools on the basis of the design of studies included in their review.
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.
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 | 0.377 | 0.814 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it