Validation of short- and long-term demographic forecasts using the Canadian Cystic Fibrosis Registry
Bibliographic record
Abstract
National cystic fibrosis (CF) data registries track patient characteristics over time and have provided insight into both emerging trends and current clinical needs. In a recent study, Burgel et al . [1] utilised the flow method, a demographic model that predicts future trends in populations, and forecasted a 50% increase in the Western European CF population by 2025, with the adult population experiencing the largest increase. Burgel et al . [2] subsequently used the French registry to validate short-term predictions; however, the accuracy of longer term projections has not been assessed. Based on the results of this study, a model that takes into consideration changing rates over time is needed to accurately estimate future populations We thank Cystic Fibrosis Canada for providing access to Canadian CF Registry data for this study, and we thank individuals living with CF and their families for allowing their data to be collected in the CF Registry to be used for clinical research. This work was presented as a poster presented at the North American Cystic Fibrosis Conference, Denver, Colorado, 18–21 October 2018.
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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 | 0.022 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".