Pain in adults with congenital heart disease - An international perspective
Bibliographic record
Abstract
Patients with adult congenital heart disease (ACHD) have many risk factors for chronic pain such as prior cardiac interventions and adult comorbidities. However, the prevalence of chronic pain has not been well described in this population. We sought to determine the prevalence of pain in a large international cohort of patients with ACHD. Data from the APPROACH-IS dataset was utilized for this study which includes 4028 patients with ACHD from 15 different countries. The prevalence of pain was assessed under the health status patient reported outcome domain utilizing the EuroQol-5D 3 level version tool. Multivariable logistic regression was used to assess differences across countries in pain, taking into account country-level random effects for clustering across observations within each country. A total of 3832 patients with ACHD met the study criteria, median age 32 years [IQR 25, 42], 52.6% females. The prevalence of at least moderate pain was reported by 28.9% (95% CO 27.5 = 30.3%) of participants. Pain was associated with country of origin, age, gender, background, education and marital status as well as several clinical variables including disease complexity, cardiac device presence, history of heart failure, psychiatric conditions and presence of other medical conditions. Those with pain had lower levels of perceived health and a lower quality of life score. Pain in patients with ACHD is common, impacting nearly one-third of patients. Given the far reaching implications of pain in patients with ACHD, further study of pain characteristics and treatment management appear warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".