An N-of-1 Study of Homeopathic Treatment of Fatigue in Patients Receiving Chemotherapy
Bibliographic record
Abstract
Background: Chemotherapy-related fatigue has been described as a subjective feeling of physical, emotional, and/or cognitive tiredness. Homeopathic treatments have the potential to relieve chemotherapy-related fatigue and are easy to deliver and demonstrate strong compliance. The N-of-1 trial design is a scientifically rigorous method of studying particular reversible clinical conditions such as chemotherapy-related side effects. Objectives : To determine whether conducting an N-of-1 trial of individualized homeopathic treatment of chemotherapy side effects is feasible. Methods: Recruitment took place at the Ottawa Integrative Cancer Clinic (OICC). Potential participants were assessed for eligibility and if eligible asked whether they would be interested in participating. Within 5 days of a chemotherapy treatment, the participant was given individualized homeopathic treatment for 14 days. As per the N-of-1 design, placebo or verum was given in randomly assigned blocks of two. Recruitment rates were monitored and changes in fatigue were measured using the Multi-dimensional Fatigue Inventory (MFI) and the EORTC-QLQ-C30. Results: Sixty-eight people were assessed between February 2014 and February 2015. Four patients were eligible for the study and one consented to participate. The one participant was enrolled in the study, followed through six cycles of chemotherapy, and completed all treatment and outcome measures. The fatigue outcome scores were inconclusive due to statistically significant differences in the baseline scores. Conclusion: While recruitment was challenging, the N-of-1 study design is feasible in this population. No conclusions on the efficacy of homeopathy can be made in this context. Study design amendments should be explored to lessen the chances of having significant baseline score differences.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".