Impact of a high emergency lung transplantation programme for cystic fibrosis in France: insight from a comparison with Canada
Bibliographic record
Abstract
BACKGROUND: France implemented a high emergency lung transplantation (HELT) programme nationally in 2007. A similar programme does not exist in Canada. The objectives of our study were to compare health outcomes within France as well as between Canada and France before and after the HELT programme in a population with cystic fibrosis (CF). METHODS: This population-based cohort study utilised data from the French and Canadian CF registries. A cumulative incidence curve assessed time to transplant with death without transplant as competing risks. The Kaplan-Meier method was used to estimate post-transplant survival. RESULTS: 10.1%), whereas deaths pre-transplant decreased from 85.3% in the pre-HELT period to 57.1% in the post-HELT period. Between 2008 and 2016, people in France were significantly more likely to receive a transplant (hazard ratio (HR) 1.56, 95% CI 1.37-1.77; p<0.001) than die (HR 0.55, 95% CI 0.46-0.66; p<0.001) compared with Canada. Post-transplant survival was similar between the countries, and there was no difference in survival when comparing pre- and post-HELT periods in France. CONCLUSIONS: Following the implementation of the HELT programme, people living with CF in France were more likely to receive a transplant than die. Post-transplant survival in the post-HELT period in France did not change compared with the pre-HELT period, despite potentially sicker patients being transplanted, and was comparable to Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".