Bibliographic record
Abstract
key principle underlying the Excellent Care for All Act was the importance of evidence in guiding decisions across the healthcare system. The Canadian Agency for Drugs and Technologies in Health (CADTH) has led pan-Canadian efforts for several years to bring evidence to decisions about what will be covered and what will not be covered in Canadian healthcare. In this interview, the CEO of CADTH – Brian O’Rourke (BO) – speaks with Charles Wright (CW) about a number of the challenges and opportunities inherent in bringing evidence to healthcare decision-making. A key point throughout the interview is the range of efforts necessary to support decision-makers as they try to bring evidence into coverage and other potentially controversial decisions. CW: Let me start by asking how you would describe the general purpose of your organization – The Canadian Agency for Drugs and Technology and Health? BO: More affectionately known in this country as CADTH – that’s the acronym to get out of your lips! We’re a health technology assessment agency. In its broadest sense, that means we inform; we provide information to policy makers in Canada regarding health technology. It can include pharmaceuticals, medical devices, medical/surgical procedures and diagnostic tests. A Bringing Evidence to Healthcare Decision Making liNKiNG eViDeNce aND Quality
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 | 0.334 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.044 | 0.042 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.027 | 0.054 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".