An appraisal of the SD <sub>IR</sub> as an estimate of true individual differences in training responsiveness in parallel‐arm exercise randomized controlled trials
Bibliographic record
Abstract
Calculating the standard deviation of individual responses (SDIR) is recommended for estimating the magnitude of individual differences in training responsiveness in parallel-arm exercise randomized controlled trials (RCTs). The purpose of this review article is to discuss potential limitations of parallel-arm exercise RCTs that may confound/complicate the interpretation of the SDIR. To provide context for this discussion, we define the sources of variation that contribute to variability in the observed responses to exercise training and review the assumptions that underlie the interpretation of SDIR as a reflection of true individual differences in training responsiveness. This review also contains two novel analyses: (1) we demonstrate differences in variability in changes in diet and physical activity habits across an intervention period in both exercise and control groups, and (2) we examined participant dropout data from six RCTs and found that significantly (P < 0.001) more participants in control groups (12.8%) dropped out due to dissatisfaction with group assignment compared to exercise groups (3.4%). These novel analyses raise the possibility that the magnitude of within-subject variability may not be equal between exercise and control groups. Overall, this review highlights that potential limitations of parallel-arm exercise RCTs can violate the underlying assumptions of the SDIR and suggests that these limitations should be considered when interpreting the SDIR as an estimate of true individual differences in training responsiveness.
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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 | 0.256 | 0.497 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".