Open-Label Placebo for the Treatment of Cancer-Related Fatigue in Patients with Advanced Cancer: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the effects of an open-labeled placebo (OLP) compared to a waitlist control (WL) in reducing cancer-related fatigue (CRF) in patients with advanced cancer using Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F). MATERIALS AND METHODS: In this randomized controlled trial, patients with fatigue ≥4/10 on Edmonton Symptom Assessment Scale (ESAS) were randomized to OLP one tablet twice a day or WL for 7 days. On day 8, patients of both arms received a placebo for 3 weeks. Changes in FACIT-F from baseline to day 8 (primary outcome) and at day 29, were assessed. Secondary outcomes included FACT-G, Multidimensional Fatigue Symptom Inventory-SF, Fatigue cluster (defined as a composite of ESAS fatigue, pain, and depression), Center for epidemiologic studies-depression, Godin leisure-time physical activity questionnaire, and global symptom evaluation. RESULTS: A total of 84/90 (93%) patients were evaluable. The mean (SD) FACIT-F change at day 8 was 6.6 (7.6) after OLP, vs. 2.1 (9.4) after WL (P = .016). On days 15 and 29, when all patients received OLP, there was a significant improvement in CRF and no difference between arms. There was also a significant improvement in ESAS fatigue, and fatigue cluster score in the OLP arm on day 8 of the study (0.029, and 0.044, respectively). There were no significant differences in other secondary outcomes and adverse events between groups. CONCLUSIONS: Open-labeled placebo was efficacious in reducing CRF and fatigue clusters in fatigued advanced cancer patients at the end of 1 week. The improvement in fatigue was maintained for 4 weeks. Further studies are needed.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".