Consensus on Exercise Reporting Template (CERT): Explanation and Elaboration Statement
Bibliographic record
Abstract
Exercise is effective for prevention and management of acute and chronic health conditions. However, trial descriptions of exercise interventions are often suboptimal, leaving readers unclear about the content of effective programmes. To address this, the 16-item internationally endorsed Consensus on Exercise Reporting Template (CERT) was developed. The aim is to present the final template and provide an Explanation and Elaboration Statement to operationalise the CERT. Development of the CERT was based on the EQUATOR Network methodological framework for developing reporting guidelines. We used a modified Delphi technique to gain consensus of international exercise experts and conducted 3 sequential rounds of anonymous online questionnaires and a Delphi workshop. The 16-item CERT is the minimum data set considered necessary to report exercise interventions. The contents may be included in online supplementary material, published as a protocol or located on websites and other electronic repositories. The Explanation and Elaboration Statement is intended to enhance the use, understanding and dissemination of the CERT and presents the meaning and rationale for each item, together with examples of good reporting. The CERT is designed specifically for the reporting of exercise programmes across all evaluative study designs for exercise research. The CERT can be used by authors to structure intervention reports, by reviewers and editors to assess completeness of exercise descriptions and by readers to facilitate the use of the published information. The CERT has the potential to increase clinical uptake of effective exercise programmes, enable research replication, reduce research waste and improve patient outcomes.
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 | 0.303 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".