The use of cannabinoids (CBs) for the treatment of chemotherapy-induced peripheral neuropathy (CIPN): A retrospective review
Bibliographic record
Abstract
e20743 Background: CIPN is a common toxicity associated with the use of chemotherapy (CT) agents such as platinums, taxanes and vinca alkaloids. Patients (pts) may suffer from pain that adversely affects their quality of life, regardless of their disease trajectory. Preclinical research has shown CBs to be effective in preventing CIPN. CBs can be beneficial for cancer pain, although their specific benefit in pts with CIPN remains unknown. Methods: A retrospective chart review was conducted to identify all pts with CIPN treated with CBs at our institution between 7/07–10/08. Results: Eight pts were identified; 6 were male. Six pts had received platinum-based CT. Four had colon cancer. Four pts had metastatic disease. The median time to CIPN treatment (post-chemotherapy) was 4 months (0–84 months). All pts had 2+ neuropathy based on the NCI-CTC for Adverse Events. Pain improved in 7/8 pts with CB treatment. In those who responded, the improvement was as much as 7 points on an 11 point VAS. Two pts who transiently stopped treatment noted increased CIPN symptoms. Three pts eventually stopped treatment (1 due to side effects). Conclusions: Treatment with CBs appears to benefit some pts with CIPN. Further research is needed to explore the optimal use of CBs in pts with CIPN. No significant financial relationships to disclose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".