Peer Review #2 of "Effects of functional correction training on injury risk of athletes: a systematic review and meta-analysis (v0.1)"
Bibliographic record
Abstract
BackgroundWe explored functional correction training using the Functional Movement Screen (FMS™) tool.We also analyzed the effects of training on the injuries of athletes in a systematic review and metaanalysis of non-randomized clinical trials. MethodologyWe collected twenty-four articles from PubMed, CENTRAL, Scopus, ProQuest, Web of Science, EBSCOhost, SPORTDiscus, Embase, WanFang, and CNKI that were published between January 1997 to September 2020.Articles were selected based on the following inclusion criteria: randomized and non-randomized controlled trials, studies with functional correction training screened by FMS™ as the independent variable, and studies with injury risk to the athlete as the dependent variable.Data conditions included the sample size, mean, standard deviation, total FMS™ scores, number of injuries, and asymmetry movement patterns after interventions in the experimental and control groups.Exclusion criteria included: conference abstracts, cross-sectional studies, articles with retrospective study design. ResultsTwelve non-randomized trials were included in the meta-analysis.The injury risk ratio of athletes after functional correction training was 0.39 RR (95% confidence interval [CI], 0.24-0.65;Z=-3.57;P=0.0003; I²=0.0%).Functional correction training was found to reduce injury risk by 60% in the experimental groups when compared with the control groups.Functional correction training improved the total FMS™ scores of athletes by 1.72 MD (95% CI, 1.50-1.93;Z=15.53;P<0.0001;I 2 =2.6%), indicating an improvement of athletes' functional patterns.Conclusion Grade B evidence indicates that functional correction training based on FMS™ may improve the functional patterns of athletes and Grade D evidence indicates that it may reduce the risk of sports injury.However, the true effect is likely to be different from the estimate of the effect.Therefore, further studies are needed to explore the influence of functional correction training on the injury risks of athletes.Protocol registration: CRD42019145287.
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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 | 0.036 | 0.179 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.012 |
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".