A descriptive analysis of clinician input and feedback into the CADTH pan-Canadian Oncology Drug Review process.
Bibliographic record
Abstract
42 Background: In Canada, through the pan-Canadian Oncology Drug Review (pCODR) process, CADTH conducts evaluations of clinical, economic, and patient evidence on cancer drugs to provide public reimbursement recommendations. The pCODR Expert Review Committee (pERC) makes these recommendations based on a drug’s overall clinical benefit, alignment with patient values, cost-effectiveness, and feasibility of adoption into the health system. Methods: In February 2016, pCODR launched a pilot process that allowed eligible clinicians (individually or as groups) not directly involved in the reviews to participate in the pCODR process, to provide: (1) input at the outset of a review; and, (2) feedback on the Initial Recommendation made by the pERC. Eligible clinicians are those who: (1) are actively practising physicians; (2) are members of a provincial cancer agency or similar body or a national cancer organization; and, (3) submit a declaration of conflict of interest. Results: As of March 31, 2018, 177 oncologists have registered to participate in the pCODR process. Of 43 submissions, 38 (88%) included clinician input. Fifteen submissions received individual input, and 33 received group input, the latter involving three to 13 clinicians or groups. Between April 2016 and March 2018, clinician input by tumour type was as follows: lung (n = 8), leukemia (n = 5), lymphoma (n = 5), gastrointestinal (n = 5), myeloma (n = 4), breast (n = 3), melanoma (n = 2), gynecological (n = 2), endocrine (n = 2), sarcoma (n = 1), and genitourinary (n = 1). Clinician input has answered several key questions, including current treatments, eligible patient populations, relevance to clinical practice, sequencing and priority of treatments, and companion diagnostic testing. Conclusions: Clinician engagement has provided value-added information on local issues from a practice perspective and insights into areas of unmet need. Continuous process improvement is important, however, and the pCODR program completed consultations in April 2018 to enhance clinician participation, proposing to: (1) customize the template that clinicians complete; and, (2) broaden the eligibility of clinicians to oncology pharmacists and oncology nurses.
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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 | 0.103 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".