<i>n</i> -of-1 Randomized Controlled Trials: An Opportunity for Complementary and Alternative Medicine Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complementary and alternative medicine (CAM) practice has traditionally relied on expert opinion and case examples to evaluate the outcome of a particular therapeutic treatment. Such trials are subject to bias, leading to the formation of erroneous conclusions about the effectiveness of most treatments. This paper reviews the feasibility of n-of-1 trials to better evaluate the clinical and statistical significance of CAM therapies. In particular: (1) problems arising from the use of standard therapeutic trials; (2) the n-of-1 trial and data analysis; (3) clinical use and advantages of the n-of-1 trial in conventional medicine; (4) potential clinical uses of the n-of-1 trial in CAM; (5) preliminary guidelines for the use of the n-of-1 trial in CAM; (6) constraints on the use of the n-of-1 trial in CAM; and (7) ethical issues in the conduct of the n-of-1 trial.
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.
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 | 0.057 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it