Early technology review: towards an expedited pathway
Bibliographic record
Abstract
OBJECTIVES: Evidence development for medical devices is often focused on satisfying regulatory requirements with the result that health professional and payer expectations may not be met, despite considerable investment in clinical trials. Early engagement with payers and health professionals could allow companies to understand these expectations and reflect them in clinical study design, increasing chances of positive coverage determination and adoption into clinical practice. METHODS: An example of early engagement through the EXCITE International model using an early technology review (ETR) is described which includes engagement with payers and health professionals to better inform companies to develop data that meet their expectations. ETR is based on an early evidence review, a framework of expectations that guides the process and identified gaps in evidence. The first fourteen ETRs were reviewed for examples of advice to companies that provided additional information from payers and health professionals that was thought likely to impact on downstream outcomes or strategic direction. Given that limitations were imposed by confidentiality, examples were genericized. RESULTS: Advice through early engagement can inform evidence development that coincides with expectations of payers and health professionals through a structured, objective, evidence-based approach. This could reduce the risk of business-related adverse outcomes such as failure to secure a positive coverage determination and/or acceptance by expert health professionals. CONCLUSIONS: Early engagement with key stakeholders exemplified by the ETR approach offers an alternative to the current approach of focusing on regulatory expectations. This could reduce the time to reimbursement and clinical adoption and benefit patient outcomes and/or health system efficiencies.
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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 | 0.529 | 0.686 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".