<scp>A</scp>lpine <scp>S</scp>kiing <scp>W</scp>ith total knee <scp>A</scp>rthro<scp>P</scp>lasty (<scp>ASWAP</scp>): metabolism, inflammation, and skeletal muscle fiber characteristics
Bibliographic record
Abstract
We investigated the effect of alpine skiing for 12 weeks on skeletal muscle characteristics and biomarkers of glucose homeostasis and cardiovascular risk factors. Twenty-three patients with a total knee arthroplasty (TKA) were studied 2.9 ± 0.9 years (mean ± SD) after the operation. Fourteen patients participated in the intervention group (IG) and nine in the control group (CG). Blood samples and muscle biopsies were obtained before (PRE) and 7.3 ± 0.8 days after (POST) the intervention, and blood samples again after a retention (RET) phase of 8 weeks. With skiing, glucose homeostasis improved in IG (decrease in fasting insulin, increase in muscle glycogen) but not in CG. Fiber type distribution and size, as well as capillary density and number of capillaries around the fibers (CAF), were not different between the operated and the non-operated leg in either group. The relative number of type I fibers increased with skiing in IG with no change in CG. Inflammatory biomarkers, plasma lipids, and mitochondrial proteins and activity did not change. Alpine skiing is metabolically beneficial and can be used as a training modality by elderly people with TKA.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".