P05.50. CONSORT extension for N-of-1 trials (CENT) guidelines
Bibliographic record
Abstract
Checklist items for the CENT guidelines were derived from three systematic reviews on N-of-1 conduct, analysis and meta-analysis. A structured process of obtaining information from a group of experts to refine and finalize the CENT guidelines was carried out. Two stages of questionnaires asked participants to rate the relative importance of suggested checklist items; the second questionnaire was refined based on feedback from the previous one. Participants included those known to have interests in either RCT reporting or N-of-1 methodology. Items included after the Delphi process were debated and finalized during an in-person meeting. There were 44 unique respondents between the two rounds of questionnaires. Based on questionnaire results, topics chosen for discussion at the in person meeting included: terminology (what is “N-of-1”?), randomization and blinding, research vs. clinical care, and analysis and meta-analysis. The CENT checklist was refined and adapted to the format of the most recent CONSORT 2010 statement. N-of-1 trials may promote an evidence-based approach to therapy so that families, health care providers and policy-makers can make informed choices, and are relevant to both conventional and complementary and alternative medicine. The CONSORT ‘extension’ will facilitate critical appraisal and interpretation of N-of-1 trials by providing authors with guidance on how to improve reporting.
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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 | 0.314 | 0.618 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.093 | 0.034 |
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".