Sustainability of Outcomes after a Randomized Crossover Trial of Resistance Exercise for Shoulder Dysfunction in Survivors of Head and Neck Cancer
Bibliographic record
Abstract
PURPOSE: Shoulder pain and dysfunction may occur after surgery for head and neck cancer (HNC) as a result of damage to or resection of the spinal accessory nerve. Previous research found that 12 weeks of upper extremity progressive resistance exercise training (PRET) improved shoulder outcomes in survivors of HNC; the purpose of this study was to determine whether benefits persisted over the longer term. METHODS: Survivors of HNC were assigned at random to PRET (n=27) or a standard therapeutic protocol (TP; n=25), with an opportunity for crossover in the TP group after 12 weeks. At 12-month follow-up, participants were mailed a questionnaire that assessed quality of life (QOL), shoulder outcomes, and exercise behaviour. RESULTS: Of the 52 participants enrolled in the study, 44 were eligible at 12-month follow-up, and 37 (71%) completed the questionnaires. Overall, self-reported outcomes were largely sustained over the follow-up period. After 12 months, regardless of original group allocation, participants who continued resistance exercise training during the follow-up period reported better neck dissection-related functioning (p=0.021) and better QOL (p=0.011) than those who did not. CONCLUSIONS: Benefits of PRET were sustained at 12-month follow-up. Ongoing participation in resistance exercise training may prove valuable as a supportive care intervention for survivors of HNC.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".