ImmunizeCA, for the Management of Patient Immunization Records: A Discussion With Dr.
Bibliographic record
Abstract
Vaccinations have “saved more lives than any other health measure ” over the past 50 years [1]. Currently, patient immunization records are managed using the yellow immunization card; however, a team of researchers at the Ottawa Hospital Research Institute (OHRI) has recently created a mobile application for all operating systems, inclusive of all provinces and territories, that conveniently stores patient data for every vaccination schedule [2]. The UOJM 2014 – 2015 Editor-in-Chief Team met with Dr. Kumanan Wilson (General Internist, The Ottawa Hospital) and Katherine Atkinson (Research Coordinator, OHRI) to discuss the development of the ImmunizeCA app [3], the first national immunization app to be endorsed by the Minister of Health, Rona Ambrose [4]. What is ImmunizeCA and what was the rationale in develop-ing the app? Dr. Wilson: This all started back in 2012. I was at the park and one of the moms expressed frustration with the yellow card for her kids, which she could never find [when] they needed to go to school. She asked me why we [could] do banking on our phones, but she couldn’t have her yellow card on her phone. I thought that was a great idea. That summer a [family friend’s]
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.025 | 0.035 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".