N-of-1 Trials: Innovative Methods to Evaluate Complementary and Alternative Medicines in Pediatric Cancer
Bibliographic record
Abstract
N-of-1 randomized controlled trials (RCTs) are randomized trials conducted within individuals and may be an attractive methodology for conducting studies of complementary and alternative medicine (CAM) in pediatric oncology. These trials may be used to determine the efficacy of an intervention in an individual, or multiple N-of-1 RCTs may be combined to estimate a population effect. There are many potential advantages to the use of N-of-1 RCTs with CAM in pediatric cancer. These advantages include the ability to determine whether CAM is effective in a specific child. In addition, the N-of-1 RCT allows parents and children to voice preferences about treatment options and allows them to directly participate in balancing adverse events and therapeutic benefits. Also, in estimation of population effects, combining multiple N-of-1 RCTs tends to require smaller sample sizes than do traditional parallel-group designs. However, there also may be several challenges to the conduct of such a trial. The use of N-of-1 RCTs may be very beneficial in evaluating CAM therapies in pediatric cancer. However, careful consideration of the advantages and disadvantages of such a design should be undertaken prior to initiating an N-of-1 RCT.
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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 | 0.307 | 0.448 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".