CONSORT extension for reporting N-of-1 trials (CENT) 2015 Statement
Bibliographic record
Abstract
N-of-1 trials provide a mechanism for making evidence based treatment decisions for an individual patient. They use key methodological elements of group clinical trials to evaluate treatment effectiveness in a single patient, for situations that cannot always accommodate large scale trials: rare diseases, comorbid conditions, or in patients using concurrent therapies. Improvement in the reporting and clarity of methods and findings in N-of-1 trials is essential for reader to gauge the validity of trials and to replicate successful findings. A CONSORT extension for N-of-1 trials (CENT 2015) provides guidance on the reporting of individual and series of N-of-1 trials. CENT provides additional guidance for 14 of the 25 items of the CONSORT 2010 checklist and recommends a diagram for depicting an individual N-of-1 trial and modifies the CONSORT flow diagram to address the flow of a series of N-of-1 trials. The rationale, development process, and CENT 2015 checklist and diagrams are reported in this document.
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 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 | 0.486 | 0.742 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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; the direct Gemma label and the distilled Codex classifier 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".