Training the next generation of clinical trial biostatisticians
Bibliographic record
Abstract
BackgroundThere is a critical shortage of biostatistics expertise and targeted training programs in clinical trials across Canada.MethodsThe Canadian Network for Statistical Training in Trials (CANSTAT), a pan-Canadian, multi-institutional training platform for biostatisticians in clinical trials, was developed to increase clinical trial biostatistics capacity in Canada.ResultsCANSTAT's training program integrates experiential learning through mentorship and placements at clinical trial sites, online workshops, and capacity-building meetings. The curriculum is designed to equip fellows with essential knowledge of clinical trials, technical skills, and practical experience necessary for their growth into professional trial biostatisticians, with several specific and measurable objectives set to achieve this goal. Educational materials, including CANSTAT competencies, reflective exercises, and individual development plans, are provided to monitor progress and ensure that fellows are meeting their academic and professional goals. Currently, CANSTAT has enrolled 19 fellows.ConclusionCANSTAT has developed a training program that equips fellows with essential skills in clinical trial design, conduct and analysis, and interprofessional communication, preparing them to effectively lead biostatistical efforts in clinical trials. By training a new generation of clinical trial biostatisticians, CANSTAT is strengthening Canada's clinical trial enterprise and improving health outcomes.
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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 | 0.129 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".