Population and Resource Utilization Among Patients With Adult Congenital Heart Disease: A Snapshot View of a Moderate-Size Canadian Regional Centre
Bibliographic record
Abstract
BACKGROUND: Health care resource utilization for patients with adult congenital heart disease (ACHD) has not been well characterized outside of large Canadian specialized regional centres. We sought to describe the ACHD population and resource utilization patterns seen in a medium regional Canadian centre providing specialized ACHD care. METHODS: A cross-sectional retrospective study was done from a sample of patients seen in 2018 at the ACHD clinic in Manitoba, Canada. Demographic data were collected along with cardiac anatomy and repair type. Health care resource utilization, clinic visits, hospital admissions, unexpected hospital presentations, and cardiac interventions were measured over a 5-year period. RESULTS: A random sample of 262 patients was selected from our specialized ACHD clinic. Mean age was 33.5 (±13.7) years; 48% of the population was female, and >50% resided within the major city limits. A total of 21% of the population had simple anatomy, 44% had moderate anatomy, and 35% had complex anatomy. The most commonly used imaging modality was echocardiography, followed by cardiac magnetic resonance imaging, with more frequent imaging done in patients with complex anatomy. Unexpected hospital encounters occurred at a rate of 16 per 100 person-years. Total inpatient hospital days occurred at a rate of 33 per 100 person-years, and visits to the congenital clinic occurred at a rate of 90 per 100 person-years. CONCLUSIONS: Health care resource utilization appears to be highest in older adults and those with more complex ACHD anatomy. As the overall cohort of adults with ACHD continues to age, resource needs are likely to increase.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".