Treatment Access, Health Economics, and the Wave of a Magic Wand
Bibliographic record
Abstract
New drugs are expensive, in part due to excessive drug development costs. Governments are trying to reduce drug prices. This can delay access to effective agents. A country's access to new drugs correlates with prices they agree to pay. After Health Canada approves a drug, the Canadian Agency for Drug and Technologies in Health (CADTH) assesses it. CADTH's approval is usually contingent on it costing ≤CAD 50,000 per quality adjusted life year (QALY) gained. This value (unchanged from the 1970s) is inappropriately low. An inflation-adjusted CAD 50,000 1975 QALY should translate into a CAD 250,000 2021 QALY. CADTH's target also does not consider that drug development costs have risen much faster than inflation or that new precision therapies may only be used in small populations. In a separate process, proposals from the Patented Medicines Price Review Board (PMPRB) would decrease initial Canadian drug prices by 20%, but prices would fall further as sales increased, with ultimate price reductions of up to 80%. PMPRB claims its proposal would not reduce drug access, but multiple analyses strongly suggest otherwise. Government price controls target the symptom (high prices), not the disease. They translate into shortages without solving the problem. CADTH and PMPRB approaches both threaten access to effective drugs.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".