Comparative effectiveness of electro-acupuncture versus gabapentin for sleep disturbances in breast cancer survivors with hot flashes: a randomized trial
Bibliographic record
Abstract
OBJECTIVE: Sleep disturbance is a major consequence of hot flashes among breast cancer survivors. This study evaluated the effects of electro-acupuncture (EA) versus gabapentin (GP) for sleep disturbances among breast cancer survivors experiencing daily hot flashes. METHODS: We analyzed data from a randomized controlled trial involving 58 breast cancer survivors experiencing bothersome hot flashes at least two times per day. Participants were randomly assigned to receive 8 weeks of EA or daily GP (total dose of 900 mg/d). The primary outcome was change in the total Pittsburgh Sleep Quality Index (PSQI) score between groups at week 8. Secondary outcomes include specific PSQI domains. RESULTS: By the end of treatment at week 8, the mean reduction in PSQI total score was significantly greater in the EA group than the GP group (-2.6 vs -0.8, P = 0.044). The EA also had improved sleep latency (-0.5 vs 0.1, P = 0.041) and sleep efficiency (-0.6 vs 0.0, P = 0.05) compared with the GP group. By week 8, the EA group had improved sleep duration, less sleep disturbance, shorter sleep latency, decreased daytime dysfunction, improved sleep efficiency, and better sleep quality (P < 0.05 for all) compared with baseline, whereas the GP group improved in duration and sleep quality only (P < 0.05). CONCLUSIONS: Among women experiencing hot flashes, the effects of EA are comparable with GP for improving sleep quality, specifically in the areas of sleep latency and efficiency. Larger randomized controlled trials with longer follow-ups are needed to confirm this preliminary finding.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".