Cannabinoids and Cancer Chemotherapy-Associated Adverse Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of cannabis is not unfamiliar to many cancer patients, as there is a long history of its use for cancer pain and/or pain, nausea, and cachexia induced by cancer treatment. To date, the US Food and Drug Administration has approved 2 cannabis-based pharmacotherapies for the treatment of cancer chemotherapy-associated adverse effects: dronabinol and nabilone. Over the proceeding decades, both research investigating and societal attitudes toward the potential utility of cannabinoids for a range of indications have progressed dramatically. The following monograph highlights recent preclinical research focusing on promising cannabinoid-based approaches for the treatment of the 2 most common adverse effects of cancer chemotherapy: chemotherapy-induced peripheral neuropathy and chemotherapy-induced nausea and vomiting. Both plant-derived and synthetic approaches are discussed, as is the potential relative safety and effectiveness of these approaches in relation to current treatment options, including opioid analgesics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it