Ontology-based Representation and Analysis of Vaccination Informed Consent
Bibliographic record
Abstract
Abstract — Although signing a vaccination (or immunization) informed consent form is not a federal requirement in the US and Canada, such a practice is required by many states and pharmacies. The content and structures of these informed consent forms vary, which make it hard to compare and analyze without standardization. To facilitate vaccination informed consent data standardization and integration, it is important to examine various vaccination informed consent forms, patient answers, and consent results. We have previously developed two related ontologies: Informed Consent Ontology (ICO) for representing general informed consent entities; Vaccine Ontology (VO) for representing vaccines, vaccine formulations, and vaccination details. Both ICO and VO are aligned with the Basic Formal Ontology (BFO) and developed by following OBO Foundry principles. In this study, we report a Vaccination Informed Consent Ontology (VICO) that integrates and extends ICO and VO to vaccination informed consent domain. Current VICO includes over 570 terms. VICO ontologically represent vaccination informed consent forms from the Walgreens and Costco pharmacies and the government of Manitoba, Canada. VICO extends ICO by adding questionnaire and question terms. The usages of VICO in combination with Semnatics Web technologies were demonstrated in two use cases using both Description Language (DL)-Query and SPARQL queries. The first use case was to compare informed consent forms from different sources. The second one was to identify vaccination contraindications based on patients ’ answers to determine who cannot be vaccinated by some vaccines. Our use cases validate that VICO is able to represent various vaccination informed consent forms, guide the report of instance consent data, support cross-source data queries. The VICO ontology is available at
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".