Reporting of Adverse Events in Muscle Strengthening Interventions in Youth: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it