Acupuncture for Chemotherapy-Induced Leukopenia: Exploratory Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Chemotherapy-induced leukopenia and neutropenia are common side effects during cancer treatment. Acupuncture has been reported as an adjunct therapy for this complication. The current study reviewed published randomized controlled trials of acupuncture's effect and explored the acupuncture parameters used in these trials. We searched biomedical databases in English and Chinese from 1979 to 2004. The study populations were cancer patients who were undergoing or had just completed chemotherapy or chemoradiotherapy, randomized to either acupuncture therapy or usual care. The methodologic quality of trials was assessed. From 33 reviewed articles, 682 patients from 11 eligible trials were included in analyses. All trials were published in non-PubMed journals from China. The methodologic quality of these trials was considerably poor. The median sample size of each comparison group was 45, and the median trial duration was 21 days. The frequency of acupuncture treatment was once a day, with a median of 16 sessions in each trial. In the seven trials in which white blood cell (WBC) counts were available, acupuncture use was associated with an increase in leukocytes in patients during chemotherapy or chemoradiotherapy, with a weighted mean difference of 1,221 WBC/muL on average (95% confidence interval 636-1,807; p < .0001). Acupuncture for chemotherapy-induced leukopenia is an intriguing clinical question. However, the inferior quality and publication bias present in these studies may lead to a false-positive estimation. Meta-analysis based on these published trials should be treated in an exploratory nature only.
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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 | 0.050 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".