SPIRIT extension and elaboration for n-of-1 trials: SPENT 2019 checklist
Bibliographic record
Abstract
Randomised clinical trials are the preferred method for establishing average intervention effects for groups. Using key methodological elements of these trials, n-of-1 trials provide rigorous evidence of intervention effects for individuals. N-of-1 trials are particularly useful for situations where randomised clinical trials are not always feasible or appropriate, such as for individuals with rare diseases, comorbid conditions, or using concurrent treatments. N-of-1 trials enhance precision when intervention effects are heterogeneous between individuals. Here, we describe an extension to the SPIRIT (standard protocol items: recommendations for interventional trials) guideline, SPENT (SPIRIT extension for n-of-1 trials), to improve the completeness and transparency of n-of-1 trial protocols. SPENT is also aligned with the CONSORT (consolidated standards of reporting trials) extension for n-of-1 trials (CENT). The guideline development group followed the development strategy for reporting guidelines endorsed by the EQUATOR Network. SPENT began with a systematic review for n-of-1 protocol recommendations. After analysis to identify possible SPENT items, a three round Delphi process was implemented, with international participation involving researchers, patient advocates, and other stakeholders. This was followed by in-person meetings and email discussion of the SPENT group to achieve consensus. SPENT has 14 extension items specific to n-of-1 trials, a checklist for n-of-1 trial protocol abstracts, and additional guidance for eight SPIRIT items where trialists could encounter issues specific to n-of-1 trials. This paper describes the rationale and development process, and provides examples and explanations for each SPENT checklist item.
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 | 0.287 | 0.510 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.053 | 0.020 |
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".