Fake news, opioids, hospital harm is the 3rd leading cause of death in Canada and the U.S., and the impact of Wynne government's health care cuts
Bibliographic record
Abstract
Media have and are reporting fake news stories about the Trump administration. Guest: Daniel Payne, who details the stories in The Federalist - Governments in Canada and the U.S. about to pass regulations limiting the amount of opioids which can be prescribed. Guest: Professor David Juurlink, head of the division of clinical pharmacology and toxicology at the University of Toronto and key advisor on opioid policy to governments Guest: Dr. Fiona Campbell, president-elect of the Canadian Pain Society. Anesthesiologist in the Department of Anesthesia and Pain Medicine at Sick Kids hospital and an associate professor at the University of Toronto - Caller Michael tells Roy about his experience living with chronic pain and how opioids are the only way life is bearable. - Hospital harm is the third leading cause of death in Canada and the United States. Guest: Kathleen Finlay, CEO and founder of the Center for Patient Protection - How have Wynne government health care cuts hurt patients and physicians? Guest: Dr. David Jacobs, Director of Coalition of Ontario DoctorsLearn more about your ad choices. Visit megaphone.fm/adchoices
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".