Effectiveness of gabapentin pharmacotherapy in chemotherapy-induced peripheral neuropathy
Bibliographic record
Abstract
OBJECTIVES: Chemotherapy-induced peripheral neuropathy (CIPN) is a common chemotherapy side effect, but its prevention and treatment remains a challenge. Neurotoxicity may lead to dose limitation or even treatment discontinuation, and therefore potentially affect the efficacy of anticancer treatment and long term outcomes. The practice to administer gabapentin for neuropathy may be applicable, but is limited by insufficient studies. The aim of our study was to assess the presence of chemotherapy-induced peripheral neuropathy in ovarian cancer patients treated with first-line paclitaxel and carboplatin chemotherapy and evaluate the effectiveness of gabapentin in treatment of this condition. MATERIAL AND METHODS: 61 ovarian cancer patients treated with first line chemotherapy were included in the study. The first phase of the study was to assess neurological condition of each patient by: neuropathy symptoms scale, McGill's scale, neurological deficit and quality of life, during the chemotherapy. In the second phase of the study we evaluated the response to gabapentin treatment in a group of patients who developed neuropathy. RESULTS: 78.7% of the patients developed chemotherapy related neuropathy. During the course of chemotherapy these patients experienced significant exacerbation of neuropathy symptoms (p < 0.0001), neuropathic pain (p < 0.0001), neurologic deficit (p < 0.0012) and worsening of quality of life (p < 0.0002). Patients who were qualified to undergo the gabapentin treatment observed improvement in symptoms (p < 0.027), pain (p < 0.027) and neurologic deficit (p < 0.019). Quality of life did not change significantly after gabapentin treatment (p < 0.128). CONCLUSIONS: Chemotherapy substantially deteriorates the neurologic condition of the patients and the quality of life. Paclitaxel and carboplatin treated patients may benefit from gabapentin therapy in chemotherapy-induced peripheral neuropathy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".