Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related death worldwide.Patients living with lung cancer often experience severe physical and psychological symptoms including dyspnea, fatigue, anxiety, decreased exercise tolerance, muscle weakness and compromised health-related quality of life as a direct consequence of the disease or as an indirect consequence of the cancer therapy itself.As both screening and treatment modalities improve, the number of people living with a diagnosis of lung cancer is increasing.Consequently, management of cancer-related symptoms as well as improvement of overall quality of life and functional status become critical issues in lung cancer patients.Thus, during the last decade, a wide range of exercise prescriptions and training modalities has been proposed and an emerging literature has addressed the effects of exercise-based rehabilitation programs along the continuum of the disease.The aim of this review is to address the latest literature regarding the feasibility and effectiveness of exercise-based rehabilitation for patients with lung cancer receiving treatments (perioperative, during chemotherapy/radiation therapy or following them) or for patients with advanced diseases.We also address how the use of new technologies or training modalities such as home-based telerehabilitation or neuromuscular electrical stimulation appears to be a promising approach to improve accessibility and participation in exercisebased rehabilitation programs.Evidence from our review suggests that pre and post-operative exercise-based rehabilitation appear to be safe and effective approaches to use with patients with lung cancer and for those with advanced disease receiving chemotherapy/radiation therapy.Larger randomized controlled trials are needed to confirm the efficacy of exercise interventions in this population.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".