Acupuncture for Hot Flashes in Cancer Patients: Clinical Characteristics and Traditional Chinese Medicine Diagnosis as Predictors of Treatment Response
Bibliographic record
Abstract
BACKGROUND: Acupuncture is a recognized integrative modality for managing hot flashes. However, data regarding predictors for response to acupuncture in cancer patients experiencing hot flashes are limited. We explored associations between patient characteristics, including traditional Chinese medicine (TCM) diagnosis, and treatment response among cancer patients who received acupuncture for management of hot flashes. METHODS: We reviewed acupuncture records of cancer outpatients with the primary reason for referral listed as hot flashes who were treated from March 2016 to April 2018. Treatment response was assessed using the hot flashes score within a modified Edmonton Symptom Assessment Scale (0-10 scale) administered immediately before and after each acupuncture treatment. Correlations between TCM diagnosis, individual patient characteristics, and treatment response were analyzed. RESULTS: The final analysis included 558 acupuncture records (151 patients). The majority of patients were female (90%), and 66% had breast cancer. The median treatment response was a 25% reduction in the hot flashes score. The most frequent TCM diagnosis was qi stagnation (80%) followed by blood stagnation (57%). Older age ( P = .018), patient self-reported anxiety level ( P = .056), and presence of damp accumulation in TCM diagnosis ( P = .047) were correlated with greater hot flashes score reduction. CONCLUSIONS: TCM diagnosis and other patient characteristics were predictors of treatment response to acupuncture for hot flashes in cancer patients. Future research is needed to further explore predictors that could help tailor acupuncture treatments for these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".