How nonrandomized trials (NRT) inform pan-Canadian Oncology Drug Review (pCODR) expert review committee (pERC) recommendations in blood cancer.
Bibliographic record
Abstract
100 Background: In Canada, the Canadian Agency for Drugs and Technologies in Health’s pERC makes reimbursement recommendations for cancer drugs based on pCODR reviews of best available evidence, which in some circumstances, is from Phase II NRTs. To date, the majority of pERC recommendations based on NRT evidence have been for blood cancers. Objective: To examine aspects of NRT evidence that may influence pERC reimbursement recommendations for blood cancers. Methods: Final pERC Recommendations on blood cancer reviews supported by NRTs were included (July 2011 to June 2018). Factors that influenced Final Recommendations, such as clinical benefit, alignment with patient values and cost-effectiveness, were extracted. Results: As of June 2018, 10 conditional and 6 negative decisions were made in 13 Final Recommendations. Among conditional reimbursement recommendations, substantial need for treatment options and poor prognosis with available therapies were commonly noted. Assessment of the feasibility of randomized controlled trials (RCT) varied. The magnitude of benefit was impressive or a substantial benefit was seen in subgroups with greater need. Some recommendations noted benefit above historical outcomes or consistent evidence with other indications or trials. Most recommendations reported an improvement in quality of life (QoL) and a manageable toxicity profile. Limitations included short trial follow-up. Factors affecting cost-effectiveness and alignment with patient values varied. Among negative recommendations, there was less certainty about burden of illness and need. Uncertainty about magnitude of clinical benefit was attributed to lack of direct or indirect comparison to available options, lack of long term data or comparison to historical evidence, limited QoL data, and variability in toxicity. All cases were not cost-effective and partially aligned with patient values. Conclusions: pERC may accept evidence from NRTs to make reimbursement recommendations for blood cancers when there is a high burden of illness, unmet need, reasonable demonstration of efficacy and manageable toxicities. Feasibility of RCT was not a consistent factor.
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 | 0.653 | 0.869 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".