059 Nonrandomised Studies as a Source of Complementary, Sequential or Replacement Evidence for Randomised Controlled Trials in Systematic Reviews and Guidelines
Bibliographic record
Abstract
Background The terms applicability, generalizability, external validity, transferability generally describe one overarching theme: can available research evidence be utilised to answer the health care questions at hand, ideally supported by a judgement about the degree of confidence in this utilisation. This concept has been called directness. Objectives To offer conceptual and practical guidance to those judging directness and using research evidence from non-randomised studies (NRS). Methods We used a literature review and feedback from participants of a workshop funded by the Agency for Healthcare Quality and Research and the Cochrane Collaboration. Results Guideline developers can use NRS as a source of complementary, sequential or replacement evidence for randomised controlled trials (RCTs) by focusing on judgements about the population, intervention, comparison and outcomes. They use NRS to complement judgements about inconsistency, the rationale and credibility of subgroup analysis, baseline risk estimates for the determination of absolute benefits and downsides, and the directness of surrogate outcomes. Authors use NRS as sequential evidence to provide evidence when the evidence from RCTs is insufficient (e.g. long-term harms). Use of evidence from NRS may also replace RCT evidence when RCTs provide indirect evidence but NRS provide overall higher quality, direct evidence. We developed a simple tool and algorithm to make judgements about indirectness more transparent. Discussions These judgements need to be made in the context of other quality of evidence domains. Implications for Guideline Developers/Users The transparency of the framework will support interaction with those making health care decision and policy.
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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 | 0.417 | 0.766 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".