Efficacy of acupuncture (ACU) therapy for cancer-related pain management in oncology patients (pts).
Bibliographic record
Abstract
196 Background: Cancer-related pain negatively affects symptom burden, morbidity, and mortality. Evidence suggests the use of ACU to relieve cancer-related pain. We investigated ACU efficacy and patient-specific factors associated with pain improvement. Methods: Medical charts were reviewed from oncology pts receiving ACU and concurrent palliative medicine management. Pre- and post-ACU pain scores, as assessed by the Edmonton Symptom Assessment Scale (ESAS), were measured at each session. Univariate logistic regression models, including an over-dispersion parameter to account for multiple observations per pt, were used to investigate the association between patient-specific variables (Table) and significant pain improvement, defined as a ≥ 2-point reduction in ESAS pain score, at each session. Results: A total of 122 ACU sessions from 53 pts were included in the analysis. Significant pain improvement was observed in 47% of all sessions (mean reduction 1.8). Baseline non-neuropathic pain was significantly associated with a higher odds of achieving pain reduction (OR 2.351; P = 0.047). Conversely, an opposite association was identified for baseline neuropathic pain (OR 0.421; P = 0.048). Age, stage, number of sessions and tumor type were not significantly associated with pain improvement, although several trends were noted (Table 1). Conclusions: ACU is an appropriate adjunct therapy for cancer-related pain, particularly for non-neuropathic pain. Larger studies to confirm patient-specific variables and further investigation into therapy related side effects will assist in determining a personalized approach to ACU therapy in the oncology population. [Table: see text]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".