CNODES: the Canadian Network for Observational Drug Effect Studies
Bibliographic record
Abstract
Although administrative health care databases have long been used to evaluate adverse drug effects, responses to drug safety signals have been slow and uncoordinated.We describe the establishment of the Canadian Network for Observational Drug Effect Studies (CNODES), a collaborating centre of the Drug Safety and Effectiveness Network (DSEN).CNODES is a distributed network of investigators and linked databases in British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and Nova Scotia.Principles of operation are as follows: (1) research questions are prioritized by the coordinating office of DSEN; (2) the linked data stay within the provinces; (3) for each question, a study team formulates a detailed protocol enabling consistent analyses in each province; (4) analyses are "blind" to results obtained elsewhere; (5) protocol deviations are permitted for technical reasons only; (6) ana lyses using multivariable methods are lodged centrally with a methods team, which is responsible for combining the results to provide a summary estimate of effect.These procedures are designed to achieve high internal validity of risk estimates and to eliminate the possibility of selective reporting of analyses or outcomes.The value of a coordinated multi-provincial approach is illustrated by projects studying acute renal injury with high-potency statins, community-acquired pneumonia with proton pump inhibitors, and hyperglycemic emergencies with antipsychotic drugs.CNODES is an academically based distributed network of Canadian researchers and data centres with a commitment to rapid and sophisticated analysis of emerging drug safety signals in study populations totalling over 40 million.
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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 | 0.019 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".