Misunderstandings, misperceptions, and mistakes
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
Discussions about evidence-based medicine (EBM) have engendered both positive and negative reactions from clinicians, researchers, and policymakers since the term was first coined in the early 1990s.1,2 These discussions were brought to the forefront again in a recent commentary by Dr Bernadine Healy, former director of National Institutes of Health, in U.S. News & World Report .3 She raised several issues that EBM practitioners and teachers face when advocating this model of care. Firstly, she stated that EBM practitioners advocate using the “best” evidence which is mostly taken from randomised trials and cost benefit studies. Secondly, she raised the issues of the interpretation of evidence for screening mammography and prostate specific antigen as examples where EBM has failed because EBM proponents did not advocate for these tests based on the available evidence. Thirdly, she likened the practice of EBM to a “straitjacket” or a cookbook approach in which both clinician judgement and patient values and circumstances are ignored. All of these criticisms of EBM stem from misperceptions or misunderstandings and can be answered by careful consideration of the definition of EBM. EBM is defined as the integration of the best available evidence with our clinical expertise and our patients’ unique values and circumstances.4 Evidence, whether strong or weak, is never sufficient to make clinical decisions. Individual values and preferences must balance this evidence to achieve optimal shared decision making. Others besides Dr Healy have stated their concern that only randomised …
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 | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.144 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.002 |
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