Adverse event reporting in exercise studies of resistance training (AERIES-RT) recommendations and toolkit: a modified Delphi process
Bibliographic record
Abstract
The objective of this study was to adapt existing adverse event (AE) reporting guidelines and develop AE Reporting In Exercise Studies of Resistance Training (AERIES-RT) recommendations and toolkit. We conducted purposeful and convenience sampling to identify researchers who published resistance training (RT) trials. We invited 80 research scientists to participate in a modified Delphi consensus process. Nineteen researchers from six countries (Canada, USA, UK, Australia, Greece, and Puerto Rico) agreed to participate. We drafted adapted AE-reporting recommendations informed by interviewing participants with common health conditions who experienced AEs after RT ( n = 12), and researchers who published RT trials ( n = 14). These recommendations were turned into a survey that was distributed electronically to the Delphi participants for rating. We conducted three rounds of voting until there was consensus (criterion: minimum 74% agreement) on each recommendation. All 19 participants responded to the three survey rounds (100% response rate). After each round, the recommendations were revised based on the participants’ feedback. For the first round, ten of 24 recommendations did not meet the criterion for consensus. For the second round, one of 28 recommendations did not meet the criterion for consensus. For the final round, the remaining recommendation met the criterion for consensus. The agreed upon recommendations were used to develop the AERIES-RT toolkit including a checklist, template AE reporting form, and a decision tree. Our modified e-Delphi consensus process resulted in developing the AERIES-RT toolkit that researchers can use to improve the frequency and accuracy of AE reporting in RT trials.
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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 | 0.630 | 0.477 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".