Reporting of Adverse Events in Muscle Strengthening Interventions in Youth: A Systematic Review
Bibliographic record
Abstract
Clear definition, identification, and reporting of adverse event (AE) monitoring during training interventions are essential for decision making regarding the safety of training and testing in youths. PURPOSE: To document the extent to which AEs, resulting from intervention studies targeting muscle strengthening training (MST) in youth, are reported by researchers. METHODS: Electronic databases (CINAHL, PubMed, SPORTDiscus, and Web of Science) were searched for English peer-reviewed articles published before April 2018. Inclusion criteria were: (1) average age <16 years, (2) use of MST, (3) statement(s) linked to the presence/absence of AEs, and (4) randomized controlled trials or quasi-experimental designs. Risk of reporting bias for AEs followed recommendations by the Cochrane Collaboration group. RESULTS: One hundred and ninety-one full-text articles were screened. One hundred and thirty met all MST criteria, out of which only 44 (33.8%; n = 1278, age = 12.1 [1.1] y) included a statement as to the presence/absence of adverse events. The 86 other studies (66.2%) included no such statement. Of the reporting 44 studies, 18 (40.1%) indicated one or more adverse events. Of the 93 reported adverse events, 55 (59.1%) were linked to training or testing. CONCLUSIONS: Most MST studies in youth do not report presence/absence of adverse events, and when reported, adverse events are not well defined.
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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 | 0.048 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".