Record linkage and big data—enhancing information and improving design
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To highlight the potential of multiple file record linkage. Linkage increases the value of existing information by supplying missing data or correcting errors in existing data, through generating important covariates, and by using family information to control for unmeasured variables and expand research opportunities. METHODS: Recent Manitoba papers highlight the use of linkage to produce better studies. Specific ways in which linkage helps deal with different substantive issues are described. RESULTS: Wide data files-files containing considerable amounts of information on each individual-generated by linkage improve research by facilitating better design. Nonexperimental work in particular benefits from such linkages. Population registries are especially valuable in supplying family data to facilitate work across different substantive fields. CONCLUSION: Several examples show how record linkage magnifies the value of information from individual projects. The results of observational studies become more defensible through the better designs facilitated by such linkage.
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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 | 0.211 | 0.563 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".