Percutaneous Peripheral Nerve Stimulation in Chemotherapy-Induced Neuropathy: A Case Report
Bibliographic record
Abstract
Background and Clinical Significance: Chemotherapy-induced peripheral neuropathy (CIPN) is a frequent and limiting complication of oncological treatment, particularly in patients receiving oxaliplatin. Its onset can significantly affect the quality of life and compromise the continuity of the antineoplastic therapy. Due to the limited efficacy of available pharmacological therapies, percutaneous electrical nerve stimulation (PENS) has been proposed as a non-invasive alternative for symptom management. Case presentation: We report the case of a 75-year-old woman with colorectal adenocarcinoma who developed CIPN following oxaliplatin administration. She underwent a 12-week course of PENS targeting the median nerve, with weekly sessions conducted without interruption of chemotherapy and without adverse effects. The patient showed progressive improvement in neurosensory symptoms, as measured by the EORTC QLQ-CIPN20 questionnaire. Quantitative sensory testing revealed normalization of thermal and vibratory sensitivity and improved mechanical detection thresholds. The cumulative oxaliplatin dose was maintained throughout treatment. Conclusions: PENS may offer an effective and safe therapeutic option for managing CIPN, enabling symptom control without compromising oncological treatment. This case supports the need for controlled clinical trials to confirm efficacy and establish standardized protocols.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".