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
BACKGROUND: N-of-1 trials (multiple crossover studies conducted in single individuals) may be ideal for determining individual treatment effects and as a tool to estimate heterogeneity of treatment effects (HTE) in a population. However, comprehensive data on n-of-1 trial methodology and analysis is lacking. We performed this study to describe n-of-1 trial characteristics, examine treatment changes resulting from n-of-1 trial participation, and to determine if trial reporting is adequate for estimating HTE. METHODS: We undertook a systematic review of n-of-1 trials published between 1985 and December 2010. Included trials were those having individual treatment episodes as the unit of randomization and reporting individual-specific treatment effects. We abstracted trial characteristics, treatment change information, and analytic methods. RESULTS: We included 108 trials reporting on 2154 participants. Approximately half (49%) of the trials used a statistical cutoff to determine a superior treatment, whereas the remainder used a graphical comparison (25%) or a clinical significance cutoff (20%). Sixty-seven trials, reporting on 488 people, provided treatment change information: 54% of participants had subsequent treatment decisions consistent with the results of the trial, 8% had decisions inconsistent with trial results, and 38% had ambiguous results. Less than half of the trials (45%) reported adequate information to facilitate the calculation of HTE. CONCLUSION: N-of-1 trials are a useful tool for enhancing therapeutic precision in a range of conditions and should be conducted more often. To facilitate future meta-analysis, and the estimation of HTE, researchers reporting n-of-1 trial results should clearly describe individual data.
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 | 0.062 | 0.839 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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