Bibliographic record
Abstract
Drug policy is a mix of scientific evidence, judgment, altruism, self-interest, and politics that are superimposed on a complex, semirational, constantly changing, overburdened system, said Andreas Laupacis, chair of the Canadian Expert Drug Advisory Committee (CEDAC). Or at least, he said, that is what it feels like in Canada. But, Laupacis added, he takes comfort in knowing that other nations are also struggling with the same complex issues of how best to evaluate the effectiveness and safety of prescription drugs and determine which medications should be publicly funded. Laupacis joined representatives from the United Kingdom, Germany, the United States, and the pharmaceutical industry at an April 22 forum in Washington, D.C., to discuss methods used in conducting systematic reviews of prescription drugs, what other information is used in determining coverage, and the public’s and other stakeholders’ roles in the process. The Capitol Hill briefing was sponsored by two nonpartisan groups: the Commonwealth Fund and the Alliance for Health Reform.
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 | 0.053 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.035 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 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".