Epidemiologic Study of Cystic Fibrosis: 25 years of observational research
Bibliographic record
Abstract
The Epidemiologic Study of Cystic Fibrosis (ESCF) was a prospective observational study of over 32,000 people with cystic fibrosis (CF) from 250 clinical care sites in North America from 1994 to 2005. Begun as a pharmacovigilance study in connection with the approval of dornase alfa in 1993, ESCF was open to all people with CF treated at any participating site in the United States or Canada. In addition to obtaining safety and effectiveness data on dornase alfa, ESCF collected encounter-based data to characterize the natural history and management of CF with a special focus on lung disease. During the study, 32,178 patients reported at least one encounter, contributing 869,136 encounters, 622,592 pulmonary function tests, 432,896 cultures, and 118,563 pulmonary exacerbations treated with intravenous antibiotics. Although ESCF data collection concluded in 2005, through a collaboration with the U.S. Cystic Fibrosis Foundation Patient Registry, additional follow-up data through 2017 was available for two-thirds of patients. This allowed for updating of CF genotype and survival information. Fifty-six peer-reviewed publications (cited over 3600 times) resulted from this study. In this manuscript we summarize the published ESCF manuscripts in thematic groups with key study findings and brief comments, and speculate on how ESCF findings will inform future data registries and patient care practices.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".