Physiotherapy Exercise Protocol Improves Pain, Quality of Life and Functional Capacity in Elderly Patients With Cancer Undergoing Palliative Care
Bibliographic record
Abstract
Background Cancer is a public health problem, especially among the older adults, and palliative care is often the most appropriate treatment. Cancer patients commonly develop immobility syndrome, mainly due to pain, reducing functional capacity. In this context, physiotherapy treatment is important for reducing symptoms and improving well-being. Aim To evaluate whether a systematized physiotherapy exercise protocol can optimize the management of cancer older patients under palliative care in terms of improving pain control and functionality, as well as the qualitative and quantitative impacts on quality of life. Methodology Twenty older adults hospitalized for 7 days and undergoing exclusive palliative care were included. The patients were evaluated on the first day and re-evaluated on the seventh day regarding pain level (visual analog scale - VAS), functionality (ICU Mobility Scale - IMS), performance status (Eastern Cooperative Oncology Group – ECOG; Karnofsky Performance Status – KPS, and Palliative Performance Scale - PPS), and perceived quality of life (Edmonton Symptom Assessment System - ESAS). The participants were submitted to a systematized protocol of physiotherapeutic exercises from the 2nd to the 6th day of hospitalization. Results Significant improvement was observed in almost all scales: VAS ( P < .001), ECOG ( P = .03), KPS ( P = .05), IMS ( P = .002) and PPS ( P = .02). The ESAS scale also showed significant improvement, except for nausea domain. Conclusion Physiotherapy protocol helped control and reduce pain and other symptoms reported by older cancer patients under palliative care, contributing to improving their quality of life and functional capacity.
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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 | 0.000 | 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".