Taking the Pulse: a Retrospective, Population-based Analysis of Alberta’s Privatization of Cardiac Testing
Bibliographic record
Abstract
BACKGROUND: Many provinces are considering an expanded role for for-profit diagnostic imaging facilities. We describe trends over time in the use of cardiac imaging studies, a subset of diagnostic imaging, in the for-profit and publicly operated facilities in Alberta. Alberta has allowed testing in private facilities since the 1970s, with formalized guidelines released in 1998. METHODS: We performed a retrospective, population-based analysis using administrative data from Alberta, Canada between 1995 and 2020 to describe the annual rates of cardiac diagnostic tests for both inpatient and outpatient settings, and trends in invasive cardiac treatments like angioplasty and coronary artery bypass grafting. RESULTS: A 3.95-fold increase in the rate of outpatient cardiac imaging per 100,000 Albertans was observed between 1998 and 2020, driven by an increase in testing at private, for-profit facilities. The rate of invasive cardiac treatments did not increase substantially over this same period. This has resulted in a net cost to Alberta of over $694 million (in 2020 dollars) in additional spending above predicted levels since 1998. CONCLUSIONS: After the implementation of imaging guidelines, a sustained and substantial increase in cardiac imaging facilities and rates was observed, including in Albertans classified as low risk for cardiac disease. A similar increase was not observed among cardiac treatment procedures, which would be anticipated if increased testing was due to changes in underlying population risk.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".