Evidence in context: one person's poison is another's acceptable risk
Bibliographic record
Abstract
Seeing a patient who might be having an adverse effect of a medication may lead to a clash of perspectives. For example, from the viewpoint of a cardiologist caring for someone with an acute coronary syndrome, it is all too easy to blame the internist or rheumatologist who prescribed a cyclo-oxygenase-2 (COX-2) inhibitor for a patient’s arthritis. This is yet another example of “practice variation,” or slow knowledge translation. However, such a judgement ignores the complexity of the evidence that putatively links COX-2 inhibitors to heart disease. To be sure, there is evidence that COX-2 inhibitors can increase risk of cardiovascular (CV) disease, but what may be overlooked is that the extent of such risk varies substantively among patients, depending on the drug, dose, dosing frequency, duration, and outcome definition. Furthermore, the balance of benefits and risks for patients depends on their unique clinical states and circumstances,1 the success or failure of alternatives, and the patient’s wishes.2 Applying the …
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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 | no category Domain: not available · Genre: Editorial 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 | high |
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 | 0.157 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.035 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".