Bibliographic record
Abstract
Our goal was to identify subgroups of adults with cystic fibrosis (CF) at low risk of death within 10 years. Factor analysis for mixed data followed by Ward9s cluster analysis was conducted using 25 variables from 1572 French CF adults in 2005. Rates of death by subgroups were analysed over 10 years. An algorithm was developed using CART (classification and regression tree) analysis to provide rules for the identification of subgroups of CF adults with low rates of death within 10 years. This algorithm was validated in 1376 Canadian CF adults. Seven subgroups were identified by cluster analysis in French CF adults, including two subgroups with low (∼5%) rates of death at 10 years: one subgroup (22% of patients) was composed of patients with nonclassic CF, the other subgroup (17% of patients) was composed of patients with classic CF but low rates of Pseudomonas aeruginosa infection and diabetes. An algorithm based on CART analysis of data in 2005 allowed us to identify most French adults with low rates of death. When tested using data from Canadian CF adults in 2005, the algorithm identified 287 out of 1376 (21%) patients at low risk (10-year death: 7.7%). Large subgroups of CF adults share low risk of 10-year mortality.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.020 |
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".