Methods for living guidelines: early guidance based on practical experience. Paper 4: search methods and approaches for living guidelines
Bibliographic record
Abstract
OBJECTIVES: To describe the key features of a continual evidence surveillance process that can be implemented for living guidelines and to outline the considerations and trade-offs in adopting different approaches. STUDY DESIGN AND SETTING: Members of the Australian Living Evidence Consortium (ALEC), National Institute of Health and Care Excellence (NICE), and the US GRADE Network (USGN) shared their practical experiences of and approaches to establishing surveillance systems for living guidelines. We identified several common components of evidence surveillance and listed the key features and considerations for each component drawn from case studies, highlighting differences with standard guidelines. RESULTS: We developed guidance that covers the initial information needed to support decisions around suitability for living mode and the practical considerations in setting up continual search surveillance systems (search frequency, sources to search, use of automation, reporting the search, ongoing resources, and evaluation). The case studies draw on our experiences with developing guidelines for COVID-19, as well as for other conditions such as stroke and diabetes, and cover a range of practical approaches, including the use of automation. CONCLUSION: This paper highlights different approaches to continual evidence surveillance that can be implemented in living guidelines.
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How this classification was reachedexpand
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 | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.264 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".