055 Use of Non-Randomised Studies In The Guideline Process: The GRADE Approach
Bibliographic record
Abstract
Background The GRADE approach to guideline development requires a review of the best available evidence which includes randomised controlled trials (RCTs) and non-randomised studies (NRS). Objectives Describe the use of NRS as a replacement, a sequence, or a complement for RCTs, in a World Health Organization guideline using the GRADE approach. Methods We searched the literature using no study type limits for the effect of screening and treatment of precancerous lesions on patient or population important outcomes and for baseline risks. We assessed quality of the evidence using GRADE. Results Depending on the outcomes, we found few to no RCTs. When there was low/very low overall quality evidence from RCTs, we used NRS studies with no independent control groups to compare proportions between groups and calculate a relative effect of treatment and this evidence replaced the RCT evidence with similar/higher quality evidence. We found no evidence in RCTs for long-term outcomes, such as spontaneous abortion. Therefore, we used data from NRS (cohort studies) for premature delivery (a surrogate) to provide sequential evidence. For evidence about baseline risk of precancerous lesions and other outcomes, we used NRS a complement to the RCT data. Discussion Data from NRS provided evidence in three ways. One key criterion to consider when grading this evidence is indirectness due to indirect comparisons, surrogate outcomes or varying population risks. Implications for Guideline Developers/Users Guideline developers can use GRADE and these methods when there is no evidence or low/very low quality evidence from RCTs.
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How this classification was reachedexpand
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 | 0.450 | 0.768 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.039 | 0.028 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.016 | 0.012 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".