Acupuncture Treatment for Chemotherapy-Induced Peripheral Neuropathy – a Case Series
Bibliographic record
Abstract
Chemotherapy induced peripheral neuropathy (CIPN) occurs in 10 to 20% of cancer patients treated with neurotoxic chemotherapy. A mixture of sensory, sensorimotor and autonomic nervous system dysfunction can occur, resulting in deterioration in function and worsened quality of life. A major feature is discomfort and pain. Early termination of treatment and dose reduction of chemotherapy may be necessary. The clinical course is variable and depends on the chemotherapy agents and their cumulative dose. Although symptoms can resolve completely, in most patients CIPN is either only partially reversible or completely irreversible. Current management for CIPN is symptomatic using membrane stabilising medications and antidepressants. The use of nerve growth factors is still experimental. Dysaesthesia and pain involving the feet and hands are described in both traditional Chinese medicine (TCM) and Western biomedicine. In TCM, the pathogenesis is related to the inability to direct Qi and Blood to the extremities, and is associated with Qi, Blood, Yang and Kidney deficiencies. Acupuncture is moderately effective in treating diabetic neuropathy. However, to date, there is no report of the usefulness of acupuncture for CIPN. We report the result of a pilot prospective case series of five patients treated with an acupuncture protocol that aims to correct Qi, Blood and Yang deficiencies and directs Qi and Blood to the extremities, with the goal of improving the symptoms of CIPN. The responses were encouraging, and cannot be easily explained by the known neurophysiological mechanisms of acupuncture.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".