Physical Therapist–Led Exercise Assessment and Counseling in Integrative Cancer Care: Effects on Patient Self-reported Symptoms and Quality of Life
Bibliographic record
Abstract
Background. Exercise - contributes to improved treatment-related outcomes. We reviewed characteristics of cancer patients referred for physical therapist–led exercise counseling at a comprehensive cancer center and its effects on self-reported symptoms and quality of life. Methods. Patients presenting for outpatient exercise counseling from February 2016 to May 2017 completed the Edmonton Symptom Assessment Scale (ESAS; 0-10 scale, 10 most severe) pre-encounter/post-encounter and Patient-Reported Outcomes Measurement Information System (PROMIS10) global health assessment pre-encounter. Counseling included assessment and education. ESAS individual items and subscales of physical distress (PHS), psychological distress, and global distress (GDS) were analyzed. We used paired t-tests to compare (1) ESAS symptoms pre-encounter/post-encounter and (2) ESAS and PROMIS10 scores at baseline (pre-encounter) and first follow-up. Results. Of 350 participants, most were female (77.7%), Caucasian (66.3%), and had breast cancer (43%). Baseline (pre-encounter) symptom scores (frequency; mean) included poor sleep (68.2%; 3.5), poor well-being (67.4%; 3.2), and fatigue (64.7%; 3.1). Comparing pre-encounter/postencounter ESAS outcomes, we observed statistically and clinically significant reduction in GDS (−3.32; SD = 6.52; P < .001). On follow-up, statistically and clinically significant improvements were observed for fatigue (−1.22; P = .01), GDS (−4.81; P = .01), PHS (−3.1; P = .03) and PROMIS10 global health, mental health, and physical health scores (all P Conclusion. Patients presenting for exercise counseling had a low to moderate symptom burden and experienced significant improvement in GDS. Longitudinal evaluation revealed improvement in global health and fatigue. Additional research is needed to learn more about patient selection and timing of exercise counseling.
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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 | 0.003 |
| 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) | 0.002 | 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".