Quality assessment of the computerized Quebec BCG vaccination registry and linkage with administrative databases : a pilot study
Bibliographic record
Abstract
The BCG (Bacillus Calmette-Gue´rin) Vaccination Registry for the Canadian province of Quebec comprises some 4 million vaccination records \nfrom 1926-1993. Its content, available in paper format, has recently been \ncomputerized using optical character recognition. In this pilot study, we \naimed to: 1) compare the computerized database with the paper format, and; \n2) determine the proportion of successful linkages with demographic and \nmedical administrative databases. For the first aim, about 0.1% of the BCG \nrecords were systematically selected from the paper format. For each record, discrepancies with the database for any of 13 variables including \nnames, birth date, gender, and characteristics of vaccination were documented. Exact agreement was observed for 99.6% of the 4,987 sampled \nrecords; no more than one error per record was present. For the second aim, \na random sample of 3,500 subjects born in 1961-1974 and vaccinated from \n1970-1974 was selected from the computerized BCG registry. Using personal identifiers (names, father’s given name, sex, and birth date), separate \nlinkages were conducted with the provincial medical insurance registration \nfile (deterministic) and birth registry (probabilistic). The proportion of successful linkages was 69.5% with the medical insurance file and 77% with \nthe birth registry, and varied greatly by birth year. In conclusion, the computerized data of the BCG registry was of excellent quality. Linkage of the \nBCG registry to administrative databases, as a first step to create a retrospective cohort, was feasible. The linkage method, birth year, and missing \nvalues in personal identifiers impacted on linkage success across year.
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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 | 0.046 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".