CONSORT extension for reporting N-of-1 trials (CENT) 2015: explanation and elaboration
Bibliographic record
Abstract
N-of-1 trials are a useful tool for clinicians who want to determine the effectiveness of a treatment in a particular individual. The reporting of N-of-1 trials has been variable and incomplete, hindering their usefulness in clinical decision making and by future researchers. This document presents the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015). CENT 2015 extends the CONSORT 2010 guidance to facilitate the preparation and appraisal of reports of an individual N-of-1 trial or a series of prospectively planned, multiple, crossover N-of-1 trials. CENT 2015 elaborates on 14 items of the CONSORT 2010 checklist, totalling 25 checklist items (44 sub-items), and recommends diagrams to help authors document the progress of one participant through a trial or more than one participant through a trial or series of trials, as applicable. Examples of good reporting and evidence based rationale for CENT 2015 checklist items are provided.
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 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 | 0.912 | 0.988 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".