Apples to apples? Comparative analyses of national CF registries
Bibliographic record
Abstract
National registries play a crucial role in understanding the natural history of cystic fibrosis (CF) and the impact of care delivery and interventions on outcomes. Data on more than 90 000 individuals are captured in CF registries around the world.1 Detailed examination of clinical characteristics, interventions and outcomes, benchmarking CF centres within jurisdictions and international comparisons across healthcare systems can stimulate quality improvement initiatives and drive effective changes in practice. In a 2015 cross-sectional comparison of the US and the UK national CF registries, children in the US had better lung function than their UK counterparts2; these differences persisted into early adulthood. However, both cohort effects and survivor bias could have affected the data interpretation as outlined in an accompanying editorial.3 In this issue of the Journal , addressing some of these confounders, Schluter and colleagues4 once again compared US and UK registry data, but this time focused on longitudinal trajectories of parallel cohorts limiting the study population to children between 6 and 18 years and those homozygous for the F508del mutation. The mean difference in per cent-predicted FEV1 (ppFEV1) between the US and UK populations at ages 6, 12 and 17 years was approximately 4%, 5% and 6%, respectively. The data suggested that children in the US may also have a slower rate of lung function decline, but the evidence for this was less convincing and dependent on …
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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 | 0.053 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".