Adjunctive nabilone in cancer pain and symptom management: a prospective observational study using propensity scoring.
Bibliographic record
Abstract
A prospective observational study assessed the effectiveness of adjuvant nabilone (Cesamet) therapy in managing pain and symptoms experienced by advanced cancer patients. The primary outcomes were the differences between treated and untreated patients at 30 days' follow-up, in Edmonton Symptom Assessment System (ESAS) pain scores, and in total morphine-sulfate-equivalent (MSE) use after adjusting for baseline discrepancies using the propensity-score method. Secondary outcomes included other ESAS parameters and frequency of other drug use. Data from 112 patients (47 treated, 65 untreated) met criteria for analyses.The propensity-adjusted pain scores and total MSE use in nabilone-treated patients were significantly lower than were those found in untreated patients (both P < 0.0001). Other ESAS parameters that improved significantly in patients receiving nabilone were nausea (P < 0.0001), anxiety (P = 0.0284) and overall distress (total ESAS score; P = 0.0208). The nabilone group showed borderline improvement in appetite (P = 0.0516). When compared with those not taking nabilone, patients using this cannabinoid had a lower rate of starting nonsteroidal anti-inflammatory agents, tricyclic antidepressants, gabapentin, dexamethasone, metoclopramide, and ondansetron and a greater tendency to discontinue these drugs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".