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 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 | 0.501 | 0.701 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".