Economic Burden and Healthcare Resource Use for Thoracic Aortic Dissections and Thoracic Aortic Aneurysms—A Population‐Based Cost‐of‐Illness Analysis
Bibliographic record
Abstract
Background Thoracic aortic dissections ( TADs ) and thoracic aortic aneurysms ( TAAs ) are resource intensive. We sought to determine economic burden and healthcare resource use to guide health policy. Methods and Results Using universal healthcare coverage data for Ontario, Canada, from 2003 to 2016, a cost‐of‐illness analysis was performed. From a single‐payer's perspective, direct costs (hospitalization, reinterventions, readmissions, rehabilitation, extended care, home care, prescription drugs, and imaging) were assessed in 2017 Canadian dollars. Controls without TADs or TAAs were matched 10:1 on age, sex, and socioeconomic status to cases with TADs or TAAs to compare posthospital service use to the general population. Linear and spline regression were used for cost trends. Total hospital costs increased from $9 M to $20.7 M for TADs ( P <0.0001) and $13 M to $18 M for TAAs ( P <0.001). Costs cumulated to $587 M for 17 113 cases. Median hospital costs for TADs were $11 525 ($6102 medical, $26 896 endograft, and $30 372 surgery) with an increase over time ( P =0.04). For TAAs , median costs were $16 683 ($7247 medical, $11 679 endograft, and $22 949 surgery) with a decrease over time ( P =0.03). Home care was the most used posthospital service ( TADs 44%, TAAs 38%), but rehabilitation had the highest median cost ( TADs $11.9 M, TAAs $11 M). Men had increased median costs for indexed hospitalizations relative to women, yet women used more posthospital services with higher service costs. Conclusions Total yearly costs have increased for TADs and TAAs . Median hospital costs have increased for TADs yet decreased for TAAs . Women use posthospital healthcare services more often than men.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".