Impact of rarity on Canadian oncology health technology assessment and funding
Bibliographic record
Abstract
OBJECTIVES: The pan-Canadian Oncology Drug Review (pCODR) evaluates new cancer drugs for public funding recommendations. While pCODR's deliberative framework evaluates overall clinical benefit and includes considerations for exceptional circumstances, rarity of indication is not explicitly addressed. Given the high unmet need that typically accompanies these indications, we explored the impact of rarity on oncology HTA recommendations and funding decisions. METHODS: We examined pCODR submissions with final recommendations from 2012 to 2017. Incidence rates were calculated using pCODR recommendation reports and statistics from the Canadian Cancer Society. Indications were classified as rare if the incidence rate was lower than 1/100,000 diagnoses, a definition referenced by the Canadian Agency for Drugs and Technologies in Health. Each pCODR final report was examined for the funding recommendation/justification, level of supporting evidence (presence of a randomized control trial [RCT]), and time to funding (if applicable). RESULTS: Of the ninety-six pCODR reviews examined, 16.6 percent were classified as rare indications per above criteria. While the frequency of positive funding recommendations were similar between rare and nonrare indication (78.6 vs. 75 percent), rare indications were less likely to be presented with evidence from RCT (50 vs. 90 percent). The average time to funding did not differ significantly across provinces. CONCLUSION: Rare indications appear to be associated with weaker clinical evidence. There appears to be no association between rarity, positive funding recommendations, and time to funding. Further work will evaluate factors associated with positive recommendations and the real-world utilization of funded treatments for rare indications.
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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 | 0.445 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.028 | 0.040 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".