Randomized Trials Using Provincial Health Numbers for Group Assignment
Bibliographic record
Abstract
Purpose: Using data from Ontario, Canada, this report shows how provincial government-assigned health card numbers can be used for individual-level randomization in large pragmatic trials. We describe how health card numbers are assigned and analyze the distribution of health card digits in a trial setting. We then provide an example of how they can be used for randomization and discuss the methodological and practical considerations of the approach. Key Findings: In Ontario, Canada, health card numbers are randomly generated and assigned without regard to the applicant's characteristics. The number is a 10-digit string connected with hyphens followed by a version code (ie, 1234-567-890-XX). The number is unique to each individual and assigned for life. Before assignment, some numbers within the 10 digits are altered using proprietary business rules. We demonstrate how to use certain digits for individual-level randomization and provide an example of how we will use the tenth digit for randomization in a large new trial of different dialysate bicarbonate concentrations. While this approach has many practical and methodological advantages, it does not allow for stratification. Before using this approach, teams should consider if it will affect the integrity of the randomization and the trial, which will be influenced by the setting and the type of intervention tested. Implications: Using provincial government-assigned health card numbers for pragmatic randomized trials appears viable, but the merits must be carefully considered on a trial-by-trial basis. The approach can streamline and reduce the cost of conducting such trials.
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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 | 0.182 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".