The Effect Of Arm Cycle Ergometry On NIRS-determined Blood Volume And Oxygenation Of Active Versus Inactive Muscles In Persons With Spinal Cord Injury.
Bibliographic record
Abstract
It is well established that blood flow to non-exercising limbs is often reduced during exercise conditions in able-bodied individuals. However the effects of arm cycle ergometry (ACE) on blood volume (HbT) and tissue oxygenation (HbO2) in exercising and non-exercising limbs above and below the level of injury have not been investigated in individuals with spinal cord injury (SCI). PURPOSE: To investigate the circulatory responses of the non-exercising leg in comparison to the exercising arm in individuals with SCI during ACE. METHODS: Four individuals (Age = 35±12 yr) with paraplegia (lesion levels T6-T12) participated in the study. Participants underwent a graded exercise test, increasing 10 watts per three minute stage until volitional fatigue. Near infrared spectroscopy was used to measure HbO2 and HbT changes in the upper (biceps brachii) and lower limb (vastus lateralis). RESULTS: A significant interaction was found for HbT between the arm and leg across incremental exercise. Arm HbT increased 401±184% while leg HbT experienced a slight decrease of 134±77% in blood flow with increasing wattage. There was also a trend (p= 0.06) towards an interaction effect for HbO2. Leg HbO2 remained relatively unchanged during submaximal exercise and then declined near maximal exercise, whereas arm HbO2 experienced a drop with increasing wattage until the last stages of exercise where it levelled off. CONCLUSION: Individuals with SCI experience a decreased HbT in the lower limb and an increased HbT in the upper limb during ACE reflecting an increased blood flow to the working limb and decreased blood flow to the non-exercising leg. These findings suggest that persons with SCI exhibit similar responses to that seen in able-bodied persons despite the lack of sympathetic innervation to the lower limb.
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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 | 0.001 |
| 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".