Resistance Training And Muscle Maintenance In A Pancreatic Cancer Pilot Study
Bibliographic record
Abstract
PURPOSE: Muscle maintenance is an important target to improve wellbeing and treatment tolerance following pancreatic cancer (PC) diagnosis. We examined muscle maintenance among PC patients participating in a home-based, tele-supervised resistance training (tele-RT) program. METHODS: Tele-RT spanned 2-4 months between clinical staging visits during or following treatment. Duration varied with treatment context. Sessions (approximately 45 min, 2x/week) targeted major muscle groups with 2 sets (10-12 repetitions) of 6 exercises using resistance tubes and handles: Squat, deadlift, chest press, row, lateral raise, and abdominal stabilization. Sessions were supervised by certified trainers via video conferences (Zoom, San Jose, CA) with progression based on perceived exertion. Skeletal muscle cross-sectional area (SMA) at the midpoint of the 3rd lumbar vertebra) was quantified using SliceOmatic (Tomovision, Magog, Canada) and computerized tomography (CT) images from clinical staging visits at enrollment (T0) and post-program follow-up (T1). RESULTS: 25 patients enrolled [56% female, median age 66 (range 43-82)]. Treatment contexts varied, with 6 patients (24%) undergoing chemotherapy for stage III-IV PC, 5 (20%) undergoing preoperative chemotherapy for stage I-II PC, 2 (8%) undergoing postoperative chemotherapy, and 12 (48%) in postoperative surveillance with no concurrent treatment. 16 patients (64%) completed at least 75% of possible sessions. 4 patients (16%; all with stage III-IV PC) dropped out due to worsening symptoms from disease progression, and 1 patient (postoperative surveillance) dropped out due to scheduling. Including all patients, there was no significant change in SMA (cm2) from T0 to T1 [mean (SD) 135.9 (34.1) vs. 134.2 (33.7), t(24)=.72, p=.5). There was a significant positive correlation between total session completion and percent SMA change [r(23)=.58, p=.002]. The 16 patients who completed at least 75% of possible sessions trended toward favorable percent SMA change compared to the 9 patients who did not [mean (SD) +1.5% (6.7) vs. -5.1% (9.4), t(23)=-1.99, p=.06]. CONCLUSIONS: Tele-RT offers promising potential to maintain or increase muscle mass following PC diagnosis but requires additional strategies to improve retention and adherence across treatment contexts. MD Anderson Cancer Center Survivorship Seed Grant
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".