Bibliographic record
Abstract
Observational studies and clinical trials in cystic fibrosis (CF) have largely been concerned with improving long-term survival. Lung function tests, in particular FEV(1), have proven to be reliable and objective measures for monitoring the course of CF lung disease. Over several decades, the variability and average rates of FEV(1) decline have been remarkably stable. In the past decade, specific treatments and management of CF have resulted in a more gradual rate of decline, so that large numbers of patients are needed to demonstrate a significant subgroup or treatment difference. New measures are needed that detect changes before lung function decline, and that reflect more subtle changes over time. As new measurement tools are developed, FEV(1) provides a model to show how age, sex, duration, and frequency of measurement are related to variability, sample size, and power in cross-sectional or longitudinal studies. Chest radiographs are a standard tool for clinical assessment of an individual patient. However, their use in clinical trials has been limited by the lack of an objective way of measuring the elements that characterize the disease process. The CT scan offers more specific measurements relating directly to the process of lung disease in CF. Computerized algorithms can provide objective scores, but it will be an ongoing challenge to confirm the validity of candidate measures and their relationship to CF lung disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".