Effects of detraining on left ventricular mass in endurance-trained individuals: a systematic review and meta-analysis
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Bibliographic record
Abstract
AIMS: Detraining refers to a loss of training adaptations resulting from reductions in training stimulus due to illness, injury, or active recovery breaks in a training cycle and is associated with a reduction in left ventricular mass (LVM). The purpose of this study was to conduct a systematic review and meta-analysis to determine the influence of detraining on LVM in endurance-trained, healthy individuals. METHODS AND RESULTS: Using electronic databases (e.g. EMBASE and MEDLINE), a literature search was performed looking for prospective detraining studies in humans. Inclusion criteria were adults, endurance-trained individuals with no known chronic disease, detraining intervention >1 week, and pre- and post-detraining LVM reported. A pooled statistic for random effects was used to assess changes in LVM with detraining. Fifteen investigations (19 analyses) with a total of 196 participants (ages 18-55 years, 15% female) met inclusion criteria, with detraining ranging between 1.4 and 15 weeks. The meta-analysis revealed a significant reduction in LVM with detraining (standardized mean difference = -0.586; 95% confidence interval = -0.817, -0.355; P < 0.001). Independently, length of detraining was not correlated with the change in LVM. However, a meta-regression model revealed length of the detraining, when training status was accounted for, was associated with the reduction of LVM (Q = 15.20, df = 3, P = 0.0017). Highly trained/elite athletes had greater reductions in LVM compared with recreational and newly trained individuals (P < 0.01). Limitations included relatively few female participants and inconsistent reporting of intervention details. CONCLUSION: In summary, LVM is reduced following detraining of one week or more. Further research may provide a greater understanding of the effects of sex, age, and type of detraining on changes in LVM in endurance-trained individuals.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.024 | 0.014 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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