Role of Exercise and Lung Function in Predicting Work Status in Cystic Fibrosis
Bibliographic record
Abstract
With larger numbers of adult patients with cystic fibrosis (CF) in the workplace, the issue of disability has arisen increasingly. We examined relationships between measures of pulmonary impairment and work/school capability and then determined whether quantification of aerobic fitness improved predictability of disease-related disability. We studied 73 patients with CF who performed lung function and exercise capacity tests, completed a work/education questionnaire, and were scored for clinical and chest radiographic status. Patients who were characterized as unemployed and in poor health had more severe pulmonary disease according to American Thoracic Society impairment/disability criteria. Subjects were further classified into three groups based on employment or education status over the preceding 12 months. FEV1, maximal oxygen consumption, Schwachman-Kulczycki clinical and Brasfield radiographic scores, and frequency of pulmonary exacerbations over 2 years were associated with disability, but change in FEV1 over 2 years and oxygen saturation at rest or exertion were not. FEV1 and Schwachman-Kulczycki scores were the best independent predictors of impairment/disability; specific thresholds used in other pulmonary diseases were of limited utility. We conclude that after accounting for either current level of FEV1 or Schwachman-Kulczycki scores, no other physiological or clinical measures contribute to predicting limitation in a work or school environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".