Taking On Tobacco: A Discussion with Dr. Andrew Pipe About His Career and The Ottawa Model for Smoking Cessation
Bibliographic record
Abstract
Dr. Andrew Pipe is chief of the division of Prevention and Rehabilitation at the University of Ottawa Heart Institute and Professor in the Faculty of Medicine at the University of Ottawa. He completed his medical training at Queen’s University, and interned at The Ottawa Hospital, beginning a career path which combined his interests in sports medicine, health promotion, and advocacy. He has been a physician for athletes at the international level, served on several sporting and anti-doping organizations, and is recognized as a leading expert on cardiovascular disease prevention, physical activity, and smoking cessation. Dr Andrew Pipe est professeur à la Faculté de médecine à l’Université d’Ottawa et il est également responsable de la division de prévention et de réhabilitation à l’Institut de cardiologie de l’Université d’Ottawa. Dr Pipe a terminé son éducation à l’Université de Queen’s et son entrainement à l’Hôpital d’Ottawa où il a commencé sa carrière dans un domaine incluant la médecine sportive, la promotion de la santé et la défense des droits. Il a été médecin pour les athlètes au niveau international et il est reconnu comme un expert sur la prévention de maladies cardiovasculaires, sur l’activité physique, et sur la cessation du tabagisme.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.028 | 0.074 |
| Insufficient payload (model declined to judge) | 0.006 | 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".