Impact of Oncology Drug Review Times on Public Funding Recommendations
Bibliographic record
Abstract
New oncology drugs undergo detailed review prior to public funding in a single-payer healthcare system. The aim of this study was to assess how cancer drug review times impact funding recommendations. Drugs reviewed by the pan-Canadian Oncology Drug Review (pCODR) between the years 2012 and 2020 were included. Data were collected including Health Canada approval dates, initial and final funding recommendations, treatment intent, drug class, clinical indications, and incremental cost-effectiveness ratios (ICER). Univariable and multivariable analyses were used to determine the association between funding recommendations and review times. Of the 164 applications submitted, 130 received a positive final recommendation. Median time from Health Canada (HC) approval to final recommendation was longer for drugs indicated for the treatment of gastrointestinal (GI) and lung cancer compared to breast, genitourinary (GU), and other tumours (205 vs. 198 vs. 111 vs. 129 vs. 181 days, respectively; Kruskal–Wallis p = 0.0312). Drugs with longer review times were more likely to receive a negative pCODR recommendation, even when adjusting for tumour type, drug class, and intent of therapy (157 vs. 298 days; Wilcoxon p = 0.0003, OR 1.002 95% CI [1.000–1.004].). There was no association between funding recommendation and tumour type or class of drug. The exploration of factors associated with variance in review times will be important in ensuring timely patient access to cancer drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".