OP83 Thinking Explicitly About Ethical Issues In Health Technology Assessment: Lessons From The Canadian Agency For Drugs And Technologies In Health
Bibliographic record
Abstract
Introduction: While methods for ethics analysis in health technology assessment (HTA) exist, there have been relatively few applications and assessments of these methods. The Canadian Agency for Drugs and Technologies in Health (CADTH) began to include an explicit analysis of ethical issues within its HTAs in 2015. To examine some of the differences among ethics analyses, we critically compared the conduct and contribution of the analysis of ethical issues for four CADTH HTAs. Methods: Two experts in ethics in HTA examined ethics analyses conducted by CADTH for four technologies: DNA mismatch repair testing for colorectal cancer, treatments for obstructive sleep apnea, dialysis for end-stage liver disease, and human papillomavirus screening for cervical cancer. The methods of analysis and presentation of results, extent to which the ethics analysis was used in committee deliberations was gathered via meeting notes, recommendation documents, and discussion, and were summarized narratively. Results: The amount of literature explicitly discussing ethical issues pertaining to particular technologies varied and was not predicted by the age and maturity of a technology. The axiological approach proved a helpful starting point for ethical reflection, but other methods were used for analysis and presentation. Explicit discussion of ethical issues identified the need for additional information to ensure robust deliberation. Committee members expressed the belief that ethics analysis “brought together” individual sections of the HTA. Conclusions: While many methods exist for ethics analysis, ethics expertise is required to identify and explicitly discuss the complete range of ethical issues relevant to a particular HTA. Ethics analyses create space to challenge assumptions underlying the clinical and economic evidence, raise issues about the value of technologies, and help to integrate the HTA results.
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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 | 0.262 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.041 | 0.085 |
| Scholarly communication | 0.036 | 0.016 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.020 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".