Effect of exercise on upper extremity pain and dysfunction in head and neck cancer survivors
Bibliographic record
Abstract
BACKGROUND: Shoulder pain and disability are well recognized complications associated with surgery for head and neck cancer. This study was designed to examine the effects of progressive resistance exercise training (PRET) on upper extremity pain and dysfunction in postsurgical head and neck cancer survivors. METHODS: Fifty-two head and neck cancer survivors were assigned randomly to PRET (n = 27) or a standardized therapeutic exercise protocol (TP) (n = 25) for 12 weeks. The primary endpoint was change in patient-rated shoulder pain and disability from baseline to postintervention. Secondary endpoints were upper extremity strength and endurance, range of motion, fatigue, and quality of life. RESULTS: Follow-up assessment for the primary outcome was 92%, and adherence to the supervised PRET and TP programs were 95% and 87%, respectively. On the basis of intention-to-treat analyses, PRET was superior to TP for improving shoulder pain and disability (-9.6; 95% confidence interval [95% CI], -16.4 to -4.5; P = .001), upper extremity strength (+10.8 kg; 95% CI, 5.4-16.2 kg; P < .001), and upper extremity endurance (+194 repetitions x kg; 95% CI, 10-378 repetitions x kg; P = .039). Changes in neck dissection impairment, fatigue, and quality of life favored the PRET group but did not reach statistical significance. CONCLUSIONS: The PRET program significantly reduced shoulder pain and disability and improved upper extremity muscular strength and endurance in head and neck cancer survivors who had shoulder dysfunction because of spinal accessory nerve damage. Clinicians should consider the addition of PRET in the rehabilitation of postsurgical head and neck cancer survivors.
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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".