Evaluation of Oral Cannabinoid-Containing Medications for the Management of Interferon and Ribavirin-Induced Anorexia, Nausea and Weight Loss in Patients Treated for Chronic Hepatitis C Virus
Bibliographic record
Abstract
OBJECTIVES: The systemic and cognitive side effects of hepatitis C virus (HCV) therapy may be incapacitating, necessitating dose reductions or abandonment of therapy. Oral cannabinoid-containing medications (OCs) ameliorate chemotherapy-induced nausea and vomiting, as well as AIDS wasting syndrome. The efficacy of OCs in managing HCV treatment-related side effects is unknown. METHODS: All patients who initiated interferon-ribavirin therapy at The Ottawa Hospital Viral Hepatitis Clinic (Ottawa, Ontario) between August 2003 and January 2007 were identified using a computerized clinical database. The baseline characteristics of OC recipients were compared with those of nonrecipients. The treatment-related side effect response to OC was assessed by c2 analysis. The key therapeutic outcomes related to weight, interferon dose reduction and treatment outcomes were assessed by Student's t test and c2 analysis. RESULTS: Twenty-five of 191 patients (13%) initiated OC use. Recipients had similar characteristics to nonrecipients, aside from prior marijuana smoking history (24% versus 10%, respectively; P=0.04). The median time to OC initiation was seven weeks. The most common indications for initiation of OC were anorexia (72%) and nausea (32%). Sixty-four per cent of all patients who received OC experienced subjective improvement in symptoms. The median weight loss before OC initiation was 4.5 kg. A trend toward greater median weight loss was noted at week 4 in patients eventually initiating OC use (-1.4 kg), compared with those who did not (-1.0 kg). Weight loss stabilized one month after OC initiation (median 0.5 kg additional loss). Interferon dose reductions were rare and did not differ by OC use (8% of OC recipients versus 5% of nonrecipients). The proportions of patients completing a full course of HCV therapy and achieving a sustained virological response were greater in OC recipients. CONCLUSIONS: The present retrospective cohort analysis found that OC use is often effective in managing HCV treatment-related symptoms that contribute to weight loss, and may stabilize weight decline once initiated.
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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 | 0.000 |
| 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) | 0.001 | 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".