Dabigatran – a case history demonstrating the need for comprehensive approaches to optimize the use of new drugs
Bibliographic record
Abstract
BACKGROUND: There are potential conflicts between authorities and companies to fund new premium priced drugs especially where there are safety and/or budget concerns. Dabigatran, a new oral anticoagulant for the prevention of stroke in patients with non-valvular atrial fibrillation (AF), exemplifies this issue. Whilst new effective treatments are needed, there are issues in the elderly with dabigatran due to variable drug concentrations, no known antidote and dependence on renal elimination. Published studies have shown dabigatran to be cost-effective but there are budget concerns given the prevalence of AF. There are also issues with potentially re-designing anticoagulant services. This has resulted in activities across countries to better manage its use. OBJECTIVE: To (i) review authority activities in over 30 countries and regions, (ii) use the findings to develop new models to better manage the entry of new drugs, and (iii) review the implications for all major stakeholder groups. METHODOLOGY: Descriptive review and appraisal of activities regarding dabigatran and the development of guidance for groups through an iterative process. RESULTS: There has been a plethora of activities among authorities to manage the prescribing of dabigatran including extensive pre-launch activities, risk sharing arrangements, prescribing restrictions, and monitoring of prescribing post-launch. Reimbursement has been denied in some countries due to concerns with its budget impact and/or excessive bleeding. Development of a new model and future guidance is proposed to better manage the entry of new drugs, centering on three pillars of pre-, peri-, and post-launch activities. CONCLUSION: Models for introducing new drugs are essential to optimize their prescribing especially where there are concerns. Without such models, new drugs may be withdrawn prematurely and/or struggle for funding.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".