The (Possible) Impact of Comparative Effectiveness Research on Pharmaceutical Industry Decision Making
Bibliographic record
Abstract
Public and private payers in rich and emerging economies are becoming increasingly interested in using evidence to inform health-care resource allocation decisions. The National Institute for Health and Clinical Excellence (NICE) in the United Kingdom has been doing so on behalf of the National Health Service (NHS) for more than 10 years. To some, NICE-type entities are barriers to access and innovation. Increasingly, however, even NICE's critics appreciate that evidence-informed decision making, carried out in a fair, inclusive, and transparent way, is better than arbitrary government-imposed price cuts. For health-care systems in developed and developing countries around the world, faced with limited budgets, there may be no third option.
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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 | 0.275 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".