Development of a mobile application, ImmunizeCA, for the management of patient immunization records: a discussion with Dr. Kumanan Wilson and Katherine Atkinson
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].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".