Achieving access: addressing the needs of payors and health technology assessment agencies: Figure 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.031 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it