PP108 Novel Approaches For Fair And Reasonable Value-Based Recommendations
Bibliographic record
Abstract
Introduction: Stakeholders from the innovation field in Québec (Canada) have collectively stressed the need to formalize the process for evaluating innovative technologies in the province. In the context of innovation, and more so for non-pharmaceutical technologies where the pace of development is rapid and the lifecycle short, evidence supporting the added value can be limited and uncertainties are common. Therefore, pragmatic approaches are needed to guide recommendations and to assure that the process is rigorous, transparent and fair. Methods: Inspired by international experiences, the Institut national d'excellence en santé et services sociaux (INESSS) has developed a novel framework, where four types of recommendations are possible (introduction, refusal, limited or conditional introduction). The starting point is an evaluation of the technology's added value, for the patient, the population and the healthcare system, and the identification of uncertainties. The value of addressing uncertainty with further research is assessed, based on the value-of-information theory, and the distinct characteristics of medical devices are taken into account (e.g. learning curve effect, irrecoverable costs and incremental innovation). Those elements interact to support the formulation of recommendations by INESSS’ advisory committee. Results: The development of the framework was an iterative process supported by the use of the preliminary framework for the assessment of several innovative technologies. Challenges with its use were identified, and led to methodological and operational improvements. So far, the experience with the framework is positive and stakeholders confirm its relevance to support fair and reasonable recommendations for innovations. Conclusions: In the rapidly changing landscape of innovation, HTA has to adapt to the challenges of assessing technologies in a context of promise and uncertainties. The framework developed by INESSS is a tool for supporting timely and fair value-based decision-making, which will benefit the healthcare system, and the patients and population it serves.
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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 | 0.238 | 0.451 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".