Findings from Evaluations of the Benefits of Diagnostic Imaging Systems
Bibliographic record
Abstract
Canada Health Infoway and its partners in the provinces and territories have made significant investments in diagnostic imaging (DI) systems across Canada. Infoway's DI Investment Program is to implement storage for diagnostic digital images so that clinicians can view images regardless of where they are stored. Specifically, Infoway is investing in Picture Archiving and Communications Systems (PACS), which are those systems with the modern digital archiving capabilities used by DI systems.eHealth implementations in Canada are responsible for demonstrating the value of eHealth investments to users and stakeholders and driving benefit optimization. Canada has a rich set of results from British Columbia, Ontario, Nova Scotia, and Newfoundland and Labrador due to early Infoway investments in DI systems. The positive evaluation results are encouraging but they indicate that continued effort and investment are required to fully realize the benefits. This paper discusses findings from evaluation studies, the pan-Canadian aggregation study, and possibilities for benefit optimization from investments made in the Electronic Health Record (EHR).
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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 | 0.030 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".