Achieving access: addressing the needs of payors and health technology assessment agencies: Figure 1
Bibliographic record
Abstract
In the current economic climate, payors are demanding more evidence of real-life effectiveness before funding drugs. Standards of evidence needed to satisfy payors may exceed regulatory standards, which in turn may vary between markets. The resulting divergence between payors, regulatory bodies, and the healthcare industry can cause uncertainty around the launch of new technologies and reduce the availability of potentially life-saving medicines. Randomized controlled trials (RCTs) remain the gold standard when investigating the safety and efficacy of a new intervention. However, real-life data are increasingly required by payors and regulatory agencies facing both straitened budgets and an abundance of new therapies competing for the same space in the market. This particularly applies to non-vitamin K antagonist oral anticoagulants—namely, the direct factor Xa inhibitors apixaban and rivaroxaban, and the direct oral thrombin inhibitor dabigatran. Despite the array of data available from RCTs, there are some areas of uncertainty around real-life use of these agents. The extent to which these drugs will be funded by payors or approved for use by regulatory agencies may therefore be centred on real-life data. This article will discuss ways in which the healthcare industry, regulatory approval bodies, payors, and patients must collaborate to find adequate solutions for generating robust evidence for the use of new interventions. We will also consider the challenges and possible solutions that may allow the healthcare industry to ensure divergent needs of stakeholders are met, to achieve a balance of clinical effectiveness and value for all.
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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 | 0.083 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.039 | 0.035 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.033 | 0.026 |
| Insufficient payload (model declined to judge) | 0.051 | 0.018 |
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".