Protocol of the CONSORT and SPIRIT Extension for multicenter clinical trials
Bibliographic record
Abstract
Background: Multicenter clinical trials play an indispensable role for assessing the efficacy of a new intervention or treatment, particularly in Phase II or III studies. Previous studies have shown that these studies often suffer from inadequate reporting of key details related to their design, implementation, and analysis, both in the protocol and final reports. This limitation reduces the practical and scientific value of the findings. Furthermore, the lack of guidance on how to report multicenter features can contribute to poor reporting. Therefore, this study aims to develop guidelines to improve the reporting of multicenter trials, including two Extensions of the CONSORT 2010 and the SPIRIT 2013. Methods/design: The standard methodology for developing health research reporting guidelines involves the following steps: (i) Identifying the need for development and launching the research project; (ii) Preparing the registration and reviewing the literatures; (iii) Proposing the initial Checklists and conducting the Delphi exercise; (iv) Arranging the consensus meeting and formulating the Checklists; (v) Conducting the pilot test and drafting explanatory documents (E&E); (vi) Seeking comments from advisory group and finalizing the guidelines; and (vii) Developing the publication and dissemination strategies. Conclusion: By using the CONSORT and SPIRIT checklists as starting points, the development of extensions specific to multicenter trials can help researchers design and report high-quality clinical research. This, in turn, can facilitate the application of study findings in the current evidence-based healthcare system.
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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 | 0.324 | 0.477 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".