Evaluation of Dual-Task Performance and Respiratory Muscle Endurance in Patients with Lung Cancer.
Bibliographic record
Abstract
Background: The onset of cancer and its treatments often lead to severe symptoms and side effects. Method: This study aims to evaluate dual-task performance, respiratory muscle strength and endurance, cognitive status, dyspnea, physical activity and quality of life in patients with lung cancer. The study included 25 lung cancer patients and 25 controls. We performed tests to assess dual-task performance, respiratory muscle strength (MIP), and endurance. Cognitive status was evaluated using the Montreal Cognitive Assessment (MOCA) Scale, physical activity level was measured with the International Physical Activity Questionnaire (IPAQ), dyspnea was measured using the Modified Medical Research Council (MMRC) Dyspnea Scale, and quality of life was evaluated with the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 Questionnaire. Results: The results indicated lung cancer patients exhibited lower MIP, respiratory muscle endurance values, and dual-task cognitive performance than the control group (p<0.05). Additionally, lung cancer patients had higher scores on the MMRC dyspnea scale (p<0.001). However, no significant differences were found in the MOCA scores or the total IPAQ score (p>0.05). Notably, there was a significant difference in the IPAQ walking score and the QLQ-C30 symptom scores, favoring the control group (p<0.05). Conclusion: In conclusion, cognitive task performance, MIP, respiratory muscle endurance, quality of life, and levels of dyspnea in individuals with lung cancer were negatively impacted. It is important to consider incorporating these parameters into the rehabilitation programs for people with lung cancer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".