Factor Analysis of Quality of Life, Dyspnea, and Physiologic Variables in Patients with Chronic Obstructive Pulmonary Disease Before and After Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To identify the relationships between quality of life (QOL) and the clinical state using factor analysis pre- and postrehabilitation. Patients with chronic obstructive pulmonary disease (COPD) suffer from a significant physiologic impairment associated with an altered QOL. Comprehensive rehabilitative programs, including exercise training, have beneficial effects on exercise tolerance and QOL for these patients. DESIGN: Factor analysis (n = 6) was conducted using the data of 32 patients with COPD. Patients had been evaluated for QOL using the Nottingham Health Profile (NHP), spirometric values, dyspnea, and the variables assessed by an incremental exercise test at three levels of activity. All measurements were obtained pre- and postrehabilitation. RESULTS: Factor analysis showed that the following two factors characterize the pathophysiologic condition of patients with COPD: (1) the specific cardiorespiratory responses to incremental exercise test and the spirometric values; and (2) the QOL results. The factor analysis results differed with the testing time (pre, post) and the level of activity. CONCLUSIONS: QOL, as evaluated by a generic questionnaire and the clinical state of patients with COPD, was independent; this independence characterized the pathophysiologic condition of our patients. Our results reinforce the usefulness of different types of evaluation, especially pre- and postrehabilitation, because they reflect independent benefits used to understand the success and follow-up of rehabilitative programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".