UpToDate adherence to GRADE criteria for strong recommendations: an analytical survey
Bibliographic record
Abstract
INTRODUCTION: UpToDate is widely used by clinicians worldwide and includes more than 9400 recommendations that apply the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. GRADE guidance warns against strong recommendations when certainty of the evidence is low or very low (discordant recommendations) but has identified five paradigmatic situations in which discordant recommendations may be justified. OBJECTIVES: Our objective was to document the strength of recommendations in UpToDate and assess the frequency and appropriateness of discordant recommendations. DESIGN: Analytical survey of all recommendations in UpToDate. METHODS: We identified all GRADE recommendations in UpToDate and examined their strength (strong or weak) and certainty of the evidence (high, moderate or low certainty). We identified all discordant recommendations as of January 2015, and pairs of reviewers independently classified them either into one of the five appropriate paradigms or into one of three categories inconsistent with GRADE guidance, based on the evidence presented in UpToDate. RESULTS: UpToDate included 9451 GRADE recommendations, of which 6501 (68.8%) were formulated as weak recommendations and 2950 (31.2%) as strong. Among the strong, 844 (28.6%) were based on high certainty in effect estimates, 1740 (59.0%) on moderate certainty and 366 (12.4%) on low certainty. Of the 349 discordant recommendations 204 (58.5%) were judged appropriately (consistent with one of the five paradigms); we classified 47 (13.5%) as good practice statements; 38 (10.9%) misclassified the evidence as low certainty when it was at least moderate and 60 (17.2%) warranted a weak rather than a strong recommendation. CONCLUSION: The proportion of discordant recommendations in UpToDate is small (3.7% of all recommendations) and the proportion that is truly problematic (strong recommendations that would best have been weak) is very small (0.6%). Clinicians should nevertheless be cautious and look for clear explanations-in UpToDate and elsewhere-when guidelines offer strong recommendations based on low certainty evidence.
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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 |
|---|---|---|---|
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 3 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".