Validating the Data Completeness and Accuracy of the Canadian Cystic Fibrosis Registry
Bibliographic record
Abstract
Introduction: The Canadian Cystic Fibrosis Registry (CCFR) was developed in the 1970s and has longitudinal demographic and clinical data on persons living with cystic fibrosis (CF) attending accredited clinics in Canada. We aimed to validate the data collection and identify potential limitations of the CCFR. Methods: Of 40 accredited CF clinics in Canada invited and based on an a priori sample size calculation, eight clinics were included. 15% of each CF clinic’s population in 2019 were randomly selected. Data variables were selected based on their importance to care, epidemiologic trends, and data related to demography, clinic visits, and hospitalizations. The accuracy of the registry data was compared to the medical records as the gold standard. Each data element was categorized as correct, incorrect, or not able to be validated. The accuracy rate was calculated as the percent correct out of all records validated. Results: A total of 4382 individuals had data entered into the CCFR in 2019. The validation cohort consisted of 208 individuals from 8 clinics, which were representative across location, size of clinic (small/medium/large), and type of clinic (adult, pediatric, and combined). The 208 individuals were 52% male and 95% White, and with a median age of 26.3 years (IQR: 15.2–36.6). Approximately 95% of CCFR data on clinical measurements, infections, treatments, and hospitalizations validated were accurate as compared to the medical record. For demography, sex and date of birth had 100% accuracy. Conclusion: Our validation of the CCFR demonstrated high accuracy for clinical and demographic variables used in clinical research.
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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 | 0.107 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| 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".