05 / THE EFFECTS OF MUSCLE RELAXATION THERAPY IN REDUCING HEAD AND NECK CANCER POSTOPERATIVE INDUCED FATIGUE AND SLEEP QUALITY -A RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
Introduction and ObjectivesFatigue and poor sleep quality is common state in patients with head and neck cancer. This study examined the effectiveness of the muscle relaxation intervention to improve the fatigue and sleep quality in head and neck cancer patients.MethodsPatients were randomized to intervention and control group (see Figure 1). The outcome adjudicators were blinded. The intervention group underwent Hendrickson muscle relaxation therapy for 15 minutes every night from the Day 3 of transfer from intensive care unit until they were discharged from hospital and the control group received regular care. We observed and recorded the all patientsu2019 status on each day. The outcomes were measured by using the Taiwanese version of the BFI (The Brief Fatigue Inventory, the scale range from 0 to 10), PSQI (Pittsburgh Sleep Quality Index, the scale range from 0 to 21) scale and the severity of depression level (Visual Analogue Scale, the range from 0 to 10).ResultsA total of 60 patients were included and analyzed(see Table 1), excluding 7 patients were dropped from the study for a few reasons. General Estimated Equation analysis revealed that the sleep quality (u03b2 = -2.07; p > 0.05) and fatigue (u03b2 = -0.80; p > 0.05) of the intervention group exhibited greater improvements than those of the control group (see Table 2). The depression level of the intervention group was significantly lower than of the control group on Days 4, Day 5, Day 7, Day 9 and Day 10 (p > 0.05) (see Figure 2).ConclusionsThe use of muscle relaxation intervention does prove to be of greater benefit in decreasing fatigue and promote sleep quality. Hence, clinical care workers could use this study as a reference for patient care.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".