Substantial and Reproducible Individual Variability in Skeletal Muscle Outcomes in the Cross-Over Designed Planica Bed Rest Program
Bibliographic record
Abstract
To evaluate the individual responses in skeletal muscle outcomes following bed rest, data from three studies (21-day PlanHab; 10-day FemHab and LunHab) were combined. Subjects (n = 35) participated in three cross-over campaigns within each study: normoxic (NBR) and hypoxic bed rest (HBR), and hypoxic ambulation (HAMB; used as control). Individual variability (SDIR) was investigated as √(SD Exp2 –SD Con2 ), where SDExp and SDCon are the standard deviations of the change score (i.e., post – pre) in the experimental (NBR and HBR) and the control (HAMB) groups, respectively. Repeatability and moderators of the individual variability were explored. Significant SDIR was detected for knee extension torque, and thigh and calf muscle area, which translated into an individual response ranging from 3 to −17% for knee extension torque, −2 to −12% for calf muscle area, and −1 to −8% for thigh muscle area. Strong correlations were found for changes in NBR vs. HBR (i.e., repeatability) in thigh and calf muscle area (r = 0.65–0.75, P < 0.0001). Change-scores in knee extension torque, and thigh and calf muscle area strongly correlated with baseline values (P < 0.001; r between −0.5 and −0.9). Orthogonal partial least squares regression analysis explored if changes in the investigated variables could predict calf muscle area alterations. This analysis indicated that 43% of the variance in calf muscle area could be attributed to changes in all of the other variables. This is the first study using a validated methodology to report clinically relevant individual variability after bed rest in knee extension torque, calf muscle area, and (to a lower extent) thigh muscle area. Baseline values emerged as a moderator of the individual response, and a global bed rest signature served as a moderately strong predictor of the individual variation in calf muscle area alterations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".