An analysis of redactions in Canada’s Common Drug Review Clinical Review Reports and how they relate to the patients’ voice
Bibliographic record
Abstract
IMPORTANCE: Canada's Common Drug Review (CDR) evaluates drug data from published and unpublished research, as well as input from patient groups, to recommend provincial coverage. Currently, the CDR process gives manufacturers the opportunity to redact information in the final publicly available report. Patients often have strong feelings regarding the efficacy, harms, health-related quality of life (HRQL), and cost associated with the drugs under review and their redacted data. Highlighting Canada's approach will hopefully build on the growing international concern regarding transparency of clinical study data. OBJECTIVE: The purpose was to objectively examine and classify completed, publicly available CDR-Clinical Review Reports (CRR) for redactions, and compare them to the patients' reported interests as patient-centred outcomes. METHODS: Two independent reviewers searched for and examined publicly available CDR-CRR from November 2013-September 2016 through the Canadian Agency for Drugs and Technologies in Health (CADTH) on-line database. Both reviewers separately classified the redactions and patient-reported interests into the following categories: efficacy, harms, HRQL and costs. All discrepancies were rectified by consensus involving a third reviewer. RESULTS: Fifty-two completed CDR-CRR were reviewed. 48 (92%) included patient-reported interests and 40 (77%) had redactions classified in the following categories: efficacy (75%), costs (48%), harms (38%), HRQL (23%). 89% of redactions were outcomes identified as patient-reported interests (69% efficacy, 42% harms, 36% cost, 33% HRQL). When examining drug characteristics, biological agents were statistically associated with increased odds of redactions with respect to either efficacy (OR 3.4, 95% CI 1.0 to 11.6) or harms (OR 3.5, 95% CI 1.02 to 12.4) compared with non-biological agents. CONCLUSIONS: Whether data from the CDR-CRR used in the decision-making should be fully disclosed to the public is controversial. Our findings suggest clinical data (efficacy, harms, HRQL) matters to patients and should be publicly available within the CDR-CRR. Canada trails Europe and the USA regarding the transparency of clinical study data. This lack of transparency relates to the patient voice, and limits movement towards patient-centred care and patient-engaged research, restricting real-world value measurement.
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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 | 0.437 | 0.785 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.064 | 0.065 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 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; 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".