Disparities in outcomes by race and ethnicity in the Canadian cystic fibrosis population
Bibliographic record
Abstract
BACKGROUND: Cystic Fibrosis has historically been described as a disease that affects people of European ancestry. Consequently, much of what we know about CF is based on evidence generated from data collected in white individuals. This may lead to systematic bias in how non-white people with CF are diagnosed and treated. In this study we compared clinical outcomes between the white and non-white people with CF in Canada. METHODS: Canadian CF Registry data collected between 2000 and 2019 were used in this population-based cohort study. Demographic characteristics and clinical outcomes of people with CF identified as white and those identified as non-white were compared. Analyses were adjusted for cohort effects but not socioeconomic status. RESULTS: Between 2000 and 2019, 5516 people with CF in the Registry were identified as white and 323 were identified as non-white. At diagnosis, the white and non-white groups were similar with respect to sex at birth, age at diagnosis, prevalence of pancreatic insufficiency, and meconium ileus. The non-white group had similar rates of CF-related complications and bacterial infections compared to the white, but worse lung function, worse nutritional status, lower treatment rates, and higher rate of hospitalizations. During the 20-year study period, the non-white group had a 1.85 higher risk of death compared to the white group (HR 95 %CI 1.39; 2.47). INTERPRETATION: There is an urgent need understand why outcomes for Canadians with CF differ between white and non-white individuals, including the role of socioeconomic circumstances.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".