Pulmonary exacerbations in cystic fibrosis
Bibliographic record
Abstract
The clinical characteristics most relevant to the decision to treat for a pulmonary exacerbation with antibiotics in cystic fibrosis patients were determined. Variables including age, increased cough frequency and sputum production, new crackles and wheezing, asthma, symptomatic sinusitis, hemoptysis, decreased lung function, weight loss, and new acquisition of Pseudomonas aeruginosa were collected in a large prospective multicenter database (Epidemiologic Study of Cystic Fibrosis). During a 12-month baseline period, data from 11692 patients were compared with data collected during the subsequent 6-month study period. Because pulmonary function assessments were unavailable for patients <6 years of age, separate analyses were done for those <6 and >or=6 years of age. The outcome of interest was any antibiotic treatment in the 6-month study period reported as indicated for an exacerbation. Characteristics with the most discriminatory power were determined using stepwise multiple logistic regression. For patients <6 years of age, the strongest independent associations with treatment for a pulmonary exacerbation were new crackles, increased cough frequency, decline in weight, and increased sputum production. For those patients >or=6 years of age, the strongest independent associations were a relative decrease in percent predicted forced expired volume in 1 sec, increased cough frequency, new crackles, and hemoptysis. The presence of three or more of these key characteristics was strongly associated with the occurrence of a treated exacerbation. The reproducibility of the model over time was confirmed by application to a subsequent set of data. This model has potential for use as an outcome measure in clinical trials, and to assist in treatment decisions for individual patients.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".