Impact of a patient-tailored complementary/integrative medicine programme on disturbed sleep quality among patients undergoing chemotherapy
Bibliographic record
Abstract
OBJECTIVES: The present study examined the impact of a patient-tailored complementary/integrative medicine (CIM) programme on sleep quality in patients undergoing chemotherapy for breast and gynaecological cancer. METHODS: Study participants received standard supportive care, with or without weekly CIM treatments. Disturbed sleep quality was defined as a score of ≥4 on the Edmonton Symptom Assessment Scale (ESAS) or a score of ≥3 on the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Adherence to integrative care was defined as attending ≥4 CIM treatments, with ≤30 days between each session. RESULTS: Of 388 eligible patients, 264 (68%) reported disturbed sleep quality. Baseline-to-follow up assessment (at 6 weeks) was optimal for 104 patients in the treatment group and for 76 controls, with 75 of treated patients found to be adherent to the CIM intervention. Sleep-related ESAS scores improved more significantly in treated patients (p=0.008), as did sleep-related concerns on EORTC (treatment group, p=0.026). CONCLUSIONS: A patient-tailored CIM programme may improve sleep quality and related concerns among patients with breast and gynaecological cancer undergoing chemotherapy. Further research is needed to better understand the impact of CIM on sleep quality in this patient population. TRIAL REGISTRATION NUMBER: NCT01860365.
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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 | 0.002 |
| 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) | 0.002 | 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".