Funding the New Biologics – Public Policy Issues in Drug Formulary Decision Making
Bibliographic record
Abstract
One function of drug formularies is to allow health care providers to exert some control over spending. Decisions about whether to include a given medication in a formulary are based on estimates of its costs and effectiveness, relative to other treatment strategies. These decisions are made from a societal perspective, as opposed to that of individual patients, which sometimes results in conflicts. The clinical response to a medication often varies widely among subjects, which means that a small subgroup of patients might benefit dramatically, while others with the same disease do not. The result would be that a drug might appear not to be cost effective in an economic analysis, even though it is of proven value for some patients. New and innovative medications are assessed according to high standards of cost effectiveness, even though established treatments are wasteful of valuable health care resources. Moreover, quality-adjusted life-years (QALYs) discriminate against certain patient groups, including those with diseases that are associated with a high morbidity but a low mortality. Such patients often incur high indirect costs, including loss of employment income and costs incurred by family caregivers that QALYs do not reflect. Therefore, even though QALYs are transparent and widely applicable, they are not necessarily appropriate in the evaluation of a particular therapeutic intervention. A new paradigm should be developed for evaluating emerging therapies. An example would be a risk-sharing approach, whereby the pharmaceutical industry and public insurers share in the costs and rewards of introducing new treatments. This would have implications for the prices charged for new medications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".