OP24 Impact Of Patient Input On Cancer Drug Funding Recommendations In Canada
Bibliographic record
Abstract
Introduction Patient involvement in health technology assessment (HTA) has documented advantages, such as improved understanding of disease context, and increased legitimacy and transparency of the HTA process. In the absence of clear metrics, thresholds, or criteria, it is not clear how input regarding patient preferences influences HTA based recommendations of the pan Canadian Oncology Drug Review (pCODR). Methods This is a concurrent complementary mixed methods study. A quantitative model (logit) is used to estimate the impact of patient input and other HTA criteria on pCODR recommendations. A qualitative analysis of semi-structured interviews with Canadian HTA committee members is used to describe the mechanisms of action through which patient input influences recommendations. Results Patient input was considered important in providing context to the HTA discussion, but committee members were not able to explicate how any specific elements of patient submissions weighted into the committee’s recommendation. There was an element of mistrust in the patient input data. The estimated impact of patient input on funding recommendations is not statistically significant, recommendations remain driven by evidence of clinical benefit. Conclusions The commitment to inclusion of patient perspectives in HTA in Canada is strong, and procedurally Canada is among the leaders in this regard. The tangible impact of patient input could be increased with an improved system for collection of most relevant data, and clear guidelines about how patient input should weigh into HTA recommendations.
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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 | 0.076 | 0.334 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".