Canadian-led capacity-building in biostatistics and methodology in cardiovascular and diabetes trials: the CANNeCTIN Biostatistics and Methodological Innovation Working Group
Bibliographic record
Abstract
The Biostatistics and Methodological Innovation Working (BMIW) Group is one of several working groups within the CANadian Network and Centre for Trials INternationally (CANNeCTIN). This programme received funding from the Canadian Institutes of Health Research and the Canada Foundation for Innovation beginning in 2008, to enhance the infrastructure and build capacity for large Canadian-led clinical trials in cardiovascular diseases (CVD) and diabetes mellitus (DM). The overall aims of the BMIW Group's programme within CANNeCTIN, are to advance biostatistical and methodological research, and to build biostatistical capacity in CVD and DM. Our program of research and training includes: monthly videoconferences on topical biostatistical and methodological issues in CVD/DM clinical studies; providing presentations on methods issues at the annual CANNeCTIN meetings; collaborating with clinician investigators on their studies; training young statisticians in biostatistics and methods in CVD/DM trials and organizing annual symposiums on topical methodological issues. We are focused on the development of new biostatistical methods and the recruitment and training of highly qualified personnel--who will become leaders in the design and analysis of CVD/DM trials. The ultimate goal is to enhance global health by contributing to efforts to reduce the burden of CVD and DM.
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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 | 0.564 | 0.615 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.015 | 0.021 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".