A method to map heterogeneity between near but non-equivalent semantic attributes in multiple health data registries
Bibliographic record
Abstract
Health registries from multiple jurisdictions often include terms that are assumed to be semantically equivalent (e.g. fetal death and stillbirth). Closer examination reveals that such attributes have near--but non-equivalent--semantics. Thus their degree of semantic heterogeneity is an important indicator of uncertainty associated with data integration between registries. We build an OWL-encoded ontology which formalizes the relationships between similar perinatal concepts found in different databases. We also introduce the concept of ontology-based metadata as a means of contextualizing such terms and linking context to the attribute data. This extended metadata are exported as XML from the health registries, and it--along with the OWL ontology--is interfaced via a web-based GUI accessible to health researchers. The GUI mapping serves as the basis for making ad hoc comparison and integration decisions. Uncertainty is addressed by precisely mapping semantic heterogeneity between fields.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".