Effect of small vs large muscle mass endurance training on maximal oxygen uptake in organ transplanted recipients
Bibliographic record
Abstract
Maximal oxygen consumption (V̇O2max) is impaired in heart (HTx), kidney (KTx), and liver (LTx) transplanted recipients and the contribution of the cardiovascular, central, and peripheral (muscular) factors in affecting V̇O2max improvement after endurance training (ET) has never been quantified in these patients. ET protocols involving single leg cycling (SL) elicit larger improvements of the peripheral factors affecting O2 diffusion and utilization than the double leg (DL) cycling ET. Therefore, this study aimed to compare the effects of SL-ET vs DL-ET on V̇O2max. We determined the DL-V̇O2max and maximal cardiac output before and after 24 SL-ET vs DL-ET sessions on 33 patients (HTx = 13, KTx = 11 and LTx = 9). The DL-V̇O2max increased by 13.8% ± 8.7 (p < 0.001) following the SL-ET, due to a larger maximal O2 systemic extraction; meanwhile, V̇O2max in DL-ET increased by 18.6% ± 12.7 (p < 0.001) because of concomitant central and peripheral adaptations. We speculate that in transplanted recipients, SL-ET is as effective as DL-ET to improve V̇O2max and that the impaired peripheral O2 extraction and/or utilization play an important role in limiting V̇O2max in these types of patients. Novelty: SL-ET increases V̇O2max in transplanted recipients because of improved peripheral O2 extraction and/or utilization. SL-ET is as successful as DL-ET to improve the cardiorespiratory fitness in transplanted recipients. The model of V̇O2max limitation indicates the peripheral factors as a remarkable limitation to the V̇O2max in these patients.
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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) | 0.001 | 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".