Characteristics of clinician input in Canadian funding decisions for cancer drugs: a cross-sectional study based on CADTH reimbursement recommendations
Bibliographic record
Abstract
OBJECTIVE: To examine characteristics of clinician input to the pan-Canadian Oncology Drug Review (pCODR) for cancer drug funding recommendations from 2016 to 2020. DESIGN, SETTING AND PARTICIPANTS: Descriptive, cross-sectional study including 62 reimbursement decisions from pCODR from 2016 to 2020. INTERVENTIONS: pCODR recommendations were analysed for the number of clinicians consulted on each submission, affiliation, number of submissions per clinician, declared financial conflicts of interest (FCOIs), randomisation, type of blinding, primary endpoint, study phase, and whether the study demonstrated improvement in overall survival (OS) and progression-free survival (PFS). MAIN OUTCOME MEASURES: The main outcome was clinician support for the initial funding recommendation. Secondary outcome measures were the association between clinician FCOIs and clinical benefit in positive recommendations. RESULTS: The study consisted of 62 submissions, in which 48 included clinician input. A total of 129 unique clinicians provided 342 consultations. The majority (59%) provided input on less than 5 submissions; however, a small proportion (4%) consulted on over 10. Nearly all clinicians were physicians (125; 96%). From the 342 consultations, 228 declared financial conflicts (67%). The most common conflicts were payments for advisory roles (51%) and honorariums (23%). Of the 48 cancer drugs under review, clinicians recommended funding 46 (96%). Only 12 (25%) demonstrated substantial benefit, according to the European Society for Medical Oncology Magnitude of Clinical Benefit Scale score. Drugs recommended for funding were more likely to have improved PFS and OS data. However, most cancer drugs supported by clinicians demonstrated no change in health-related quality of life (HRQoL), including one that demonstrated worsened HRQoL. There was no statistically significant difference between FCOI status and recommending drugs with health gains. CONCLUSION: Clinicians offer crucial information on funding decisions. However, we found clinicians strongly supported funding nearly all cancer drugs under review, despite most not offering substantial benefit to patients nor gains in quality of life. While these drugs might be helpful options in clinical practice, funding numerous cancer drugs may be unsustainable for public health systems.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".