Systematic reviews of clinical practice guidelines: a methodological guide
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
OBJECTIVES: Systematic reviews (SRs) of clinical practice guidelines (CPGs) are unique knowledge syntheses that require tailored approaches to, and greater subjectivity in, design and execution compared with other SRs in clinical epidemiology. We provide review authors structured direction on how to design and conduct methodologically rigorous SRs of CPGs. STUDY DESIGN AND SETTING: A guidance paper outlining suggested methodology for conducting all stages of an SR of CPGs. We present concrete examples of approaches used by published reviews, including a case exemplar demonstrating how this methodology was applied to our own SR of CPGs. RESULTS: Review context and the unique characteristics of CPGs as research syntheses or clinical guidance statements must be considered in all aspects of review design and conduct. Researchers should develop a "PICAR" statement to help form and focus on the research question(s) and eligibility criteria, assess CPG quality using a validated appraisal tool, and extract, analyze, and summarize data in a way that is cogent and transparent. CONCLUSION: SRs of CPGs can be used to systematically identify, assess, and summarize the current state of guidance on a clinical topic. These types of reviews often require methodological tailoring to ensure that their objectives and timelines are effectively and efficiently addressed; however, they should all meet the criteria for an SR, follow a rigorous methodological approach, and adhere to transparent reporting practices.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.537 | 0.992 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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