Being ‘evidence-based’ in the absence of evidence: The management of non-evidence in guideline development
Bibliographic record
Abstract
Since the emergence of the Evidence-Based Medicine (EBM) movement, the nature and role of evidence in medicine has been much debated. The formal classification of evidence that is unique to Evidence-Based Medicine, referred to as the Evidence hierarchy, has been fiercely criticized. Yet studies that examine how Evidence is classified in EBM practice are rare. This article presents an observational study of the nature of Evidence and Evidence-Based Medicine as understood and performed in practice. It does this by examining how an absence of Evidence is defined and managed in Evidence-Based Guideline development. The EBM label does not denote the quantity or quality of evidence found, but the specific management of the absence of evidence, requiring a transparently reported process of evidence searching, selection and presentation. I propose the term ‘Evidence Searched Guidelines’ to better capture this specific way of ‘being’ EBM. Moreover, what counts as Evidence depends not just on the Evidence hierarchy, but requires agreement between the members of each guideline development group who mobilize a range of ‘other’ knowledges, such as biological principles and knowledge of the clinic. In addition, I distinguish four non-Evidentiary justifications that are relied upon in the formulation of recommendations (literature, qualified opinions, ethical principles, and practice standards). These are not always secondary to Evidence but may be positioned outside the hierarchy, allowing them to trump Evidence. The legitimacy of Evidence-Based Medicine relies neither on experts nor numbers, but on distinct procedures for handling (non-)Evidence, reflecting its ‘regulatory objectivity’. Finally, the notion of transparency is central for understanding how Evidence-Based Medicine regulates, and is regulated within, contemporary biomedicine.
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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 | Science and technology studiesMetaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.657 | 0.836 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.017 | 0.080 |
| Scholarly communication | 0.063 | 0.063 |
| Open science | 0.017 | 0.057 |
| Research integrity | 0.035 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 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 2 models reading the full record.
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".