Adapt or die: how the pandemic made the shift from EBM to EBM+ more urgent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evidence-based medicine (EBM's) traditional methods, especially randomised controlled trials (RCTs) and meta-analyses, along with risk-of-bias tools and checklists, have contributed significantly to the science of COVID-19. But these methods and tools were designed primarily to answer simple, focused questions in a stable context where yesterday's research can be mapped more or less unproblematically onto today's clinical and policy questions. They have significant limitations when extended to complex questions about a novel pathogen causing chaos across multiple sectors in a fast-changing global context. Non-pharmaceutical interventions which combine material artefacts, human behaviour, organisational directives, occupational health and safety, and the built environment are a case in point: EBM's experimental, intervention-focused, checklist-driven, effect-size-oriented and deductive approach has sometimes confused rather than informed debate. While RCTs are important, exclusion of other study designs and evidence sources has been particularly problematic in a context where rapid decision making is needed in order to save lives and protect health. It is time to bring in a wider range of evidence and a more pluralist approach to defining what counts as 'high-quality' evidence. We introduce some conceptual tools and quality frameworks from various fields involving what is known as mechanistic research, including complexity science, engineering and the social sciences. We propose that the tools and frameworks of mechanistic evidence, sometimes known as 'EBM+' when combined with traditional EBM, might be used to develop and evaluate the interdisciplinary evidence base needed to take us out of this protracted pandemic. Further articles in this series will apply pluralistic methods to specific research questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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 it