Bibliographic record
Abstract
plant, are synthetic analogues, or occur endogenously. Although cannabinoids interact mostly at the level of the currently recognized cannabinoid receptors, they might have cross reactivity, such as at opioid receptors. Patients with malignant disease represent a cohort within health care that have some of the greatest unmet needs despite the availability of a plethora of guideline-driven disease-modulating treatments and pain and symptom management options. Cannabinoid therapies are varied and versatile, and can be offered as pharmaceuticals (nabilone, dronabinol, and nabiximols), dried botanical material, and edible organic oils infused with cannabis extracts. Cannabinoid therapy regimens can be creative, involving combinations of all of the aforementioned modalities. Patients with malignant disease, at all points of their disease trajectory, could be candidates for cannabinoid therapies whether as monotherapies or as adjuvants. The most studied and established roles for cannabinoid therapies include pain, chemotherapy-induced nausea and vomiting, and anorexia. Moreover, given their breadth of activity, cannabinoids could be used to concurrently optimize the management of multiple symptoms, thereby reducing overall polypharmacy. The use of cannabinoid therapies could be effective in improving quality of life and possibly modifying malignancy by virtue of direct effects and in improving compliance or adherence with disease-modulating treatments such as chemotherapy and radiation therapy.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".