From Hierarchy to Network: a richer view of evidence for evidence-based medicine
Bibliographic record
Abstract
Evidence-based medicine (EBM) advocates the improvement of patient care through the use of current best research evidence in medical decision making. In practice, "best evidence" generally refers to where a study fits on a hierarchy of evidence, which places randomized controlled trials (RCTs) and other population-level research above laboratory research. Because population research is concerned primarily with average results obtained from large groups of people, ranking evidence on the basis of its place in the hierarchy is shortsighted and ultimately limits the ability of research results to inform the care of individual patients. The history and methodology of epidemiology reveals a close relationship between population-level and laboratory research; both types of research are necessary if we are to understand the causes of a disease. What EBM does not take into account in its hierarchy of evidence is that the same thing is true for research on the safety and efficacy of medical interventions. To maximize the information that clinical research can provide for clinical care, RCTs should be designed to elucidate within-group variability. This can only be done if the hierarchy of evidence is replaced by a network that takes into account the relationship between epidemiological and laboratory research.
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.
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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".