Canadian Data Platform: Developing an Algorithm Inventory for Health and Social Measures
Bibliographic record
Abstract
IntroductionThe SPOR (Strategy for Patient-Oriented Research) Canadian Data Platform aims to facilitate multi-jurisdictional research through a variety of activities, including the development of standardized algorithms for health conditions, health service use, and the determinants of health. An initial step towards standardization was to identify existing health measures that have been validated or assessed for feasibility of implementation in multi-jurisdictional research, document features of these measures, and describe the methods used to validate or assess feasibility. Objectives and ApproachWe constructed an inventory of published algorithms to measure population health, health services use, and the determinants of health. A systematic review of published literature identified algorithms from validation or feasibility studies in two or more Canadian provinces/territories. The search strategy was applied to Medline, Embase, and Scopus. The Algorithms and Harmonized Data Working Group of the Canadian Data Platform identified relevant fields for data extraction, including study type, population characteristics, data source, jurisdictions, and algorithm details. A searchable online resource was created to maintain and share the algorithms. ResultsOf the 2758 articles retrieved, 1998 articles underwent title and abstract review and 60 articles were selected for full review. A total of 8 validation and 26 feasibility studies were assessed; they contributed over 140 algorithms. Chronic physical health conditions, such as diabetes, depression, hypertension and dementia, were most often represented in the algorithms. British Columbia and Manitoba were the jurisdictions most frequently represented in the studies. Methods to facilitate automated searching of the on-line resource are under development. Conclusion / ImplicationsOur inventory of algorithms provides valuable information for researchers interested in conducting multi-jurisdictional studies, and reveals gaps where further algorithm development could be undertaken. This comprehensive collection of existing algorithms will support future studies aimed at improving population health and monitoring health service use in Canada.
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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 | 0.114 | 0.288 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.041 | 0.058 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".