Elevated Rates and Earlier Onset of Nonpulmonary Comorbidities in Adults with Cystic Fibrosis: A Population-based Study
Bibliographic record
Abstract
Abstract Rationale People with cystic fibrosis (pwCF) are living longer with increasing comorbidities. Objectives To estimate the rate of emerging nonpulmonary comorbidities in adults with cystic fibrosis (CF) and to compare these rates with the non-CF population. Methods This is a population-based cohort study of adults using Canadian Cystic Fibrosis Registry data linked with health administrative databases in Ontario. Cases of cardiovascular disease (CVD) and symptomatic kidney stones were identified using diagnostic and procedural codes. Chronic kidney disease (CKD) was defined as estimated glomerular filtration rate <60 ml/min/1.73 m2. Cancer cases were obtained using the Ontario Cancer Registry. Poisson regression was used to estimate the rates per 1,000 person-years of follow-up. Results The age- and sex-adjusted rates of CVD, CKD, kidney stones, and cancer per 1,000 person-years in the non–lung transplantation cohort were 24.5 (95% confidence interval [CI], 21.5–28.0), 3.7 (95% CI, 2.7–5.2), 7.4 (95% CI, 6.1–9.0), and 5.8 (95% CI, 4.5–7.6) respectively. pwCF who underwent lung transplantation had higher rates of all four conditions, and cancer and CKD occurred earlier compared with the nontransplantation cohort. When comparing the CF and non-CF populations, pwCF without lung transplantation had higher age- and sex-adjusted rates of CVD (relative risk [RR], 2.9 [95% CI, 2.6–3.4]), CKD (RR, 2.1 [95% CI, 1.5–2.9]), kidney stones (RR, 2.9 [95% CI, 2.4–3.6]), and cancer (RR, 1.9 [95% CI, 1.5–2.5]). These events occurred at a median age of at least 20 years earlier in the CF cohort. In the post-transplantation population, there were no significant differences in the rates of CVD, kidney stones, and cancers between pwCF and the non-CF population, but events occurred earlier in pwCF. Conclusions Nonpulmonary complications occur at a high rate and at a younger age in pwCF compared with the non-CF population, which highlights the importance of incorporating these issues in CF care models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".