OP145 The Release Of The Fourth Edition Canadian Agency For Drugs And Technologies In Health (CADTH) Economic Guidelines – A Year In Review
Bibliographic record
Abstract
Introduction: In March 2017, CADTH released the fourth edition of the Guidelines for the Economic Evaluations of Health Technologies. As part of the update a few notable changes were made to topics such as discount rate, target population, modeling, effectiveness, analysis, and the theoretical foundations for the Guidelines. In this presentation we will describe: the implementation of the Guidelines; approaches taken to facilitate the adoption of the Guideline statements; and the tools provided to assist users and doers in using cost-effectiveness information in healthcare decision making. Methods: Given some of the changes made to the Guidelines, CADTH identified the need to engage stakeholders early in preparation for the release of the fourth edition. Feedback on topics was sought from various stakeholders (researchers in the field, industry, patient groups, and decision makers) throughout the process. Also, suggestions for tools to support the understanding and implementation of the Guidelines were noted by CADTH. To further support use of the Guidelines, CADTH committed to undertake a number of activities including: workshops for decision makers and researchers; worked examples to illustrate the approaches; and development of tools to assist in the use of recommended methods. Updates to align drug submission guidance with the Guidelines are ongoing. Results: The final version of the Guidelines was greatly influenced by the stakeholder feedback received, with a focus on greater clarity. Whilst efforts to increase acceptance and adoption of the guidelines are ongoing, we present preliminary findings with respect to engagement with stakeholders and adoption of new guidance in drug submissions. Conclusions: The plan to engage stakeholders continues to be effective. As such, there has been general acceptance of the changes and an interest in education and tools to assist with implementation of the Guidelines.
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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 | 0.030 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".