Exercise counseling in integrative oncology: Patient characteristics and effects on self-reported symptoms.
Bibliographic record
Abstract
10089 Background: Physical activity and exercise have shown benefits for cancer prevention and contribute to improved treatment related outcomes. We reviewed the characteristics of cancer patients referred for physical therapist-led exercise counseling at a comprehensive cancer center and its effects on self-reported symptoms. Methods: Patients presenting for an exercise counseling consultation and follow up encounter at an Integrative Medicine Center outpatient clinic from Feb 2016 to May 2017 completed the Edmonton Symptom Assessment Scale (ESAS; 0-10 scale, 10 most severe) pre/post-encounter; a PROMIS 10 global health assessment was also completed within 30 days of each encounter. Exercise counseling was provided by a physical therapist. ESAS individual items and subscales of Physical Distress (PHS), Psychological Distress (PSS), and Global Distress (GDS) were analyzed. We used paired t-tests with a p-value correction (i.e., p < .001) to examine symptoms before and after each encounter. Results: Data were available for 367 participants; 68 (18.5%) had at least one follow up encounter at 56.1 days (mean). Most were female (77.7%), caucasian (66.2%) with breast (43%), gastrointestinal (15.8%), or gynecologic (6.8%) cancer. Highest and most frequently reported (%) symptom scores at baseline included poor sleep (63.2%; 3.55), fatigue (59.9%; 3.19), and poor well-being (62.4%; 3.17). Pre/post change for one encounter was statistically and clinically significant for the ESAS GDS subscale (-3.36, SD 6.54, p < 0.001). ESAS change (n = 40) between baseline and first follow up within 60 days was statistically and clinically significant for improvement in fatigue (-1.35, p = 0.006), GDS (-4.55, p = 0.008), and PHS (-3.15, p = 0.016). PROMIS10 scores (n = 321) at baseline included global health 31.34. For the follow up group (n = 67), significant improvements were observed for mental health (p = 0.015) and global health (p = 0.024), not physical health (p = 0.07). Conclusions: Patients presenting for an exercise consultation had a moderate symptom burden with global health scores lower than the population mean, with improvements observed in global distress, physical distress, and fatigue after one encounter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".