Predicting Peak Oxygen Uptake From Submaximal Exercise After Spinal Cord Injury
Bibliographic record
Abstract
PURPOSE: To determine the validity of the six-minute arm test (6MAT) in predicting peak oxygen consumption (VO2peak) in individuals with chronic spinal cord injury (SCI). METHODS: Fifty-two individuals with chronic SCI (age 38±10 years; American Spinal Injury Association Impairment Scale A-D, neurological level of injury C1-L2, years post-injury 13±10 years) completed an incremental arm VO2peak test and a submaximal 6MAT. Oxygen consumption data from both tests were used to create a predictive equation with regression analysis. Subsequently, a cross-validation group of an additional 10 individuals with SCI (age 39±13 years; AIS A-D, NLI C3-L3, YPI 9±9 years) were used to determine the predictive power of the equation. RESULTS: All subjects were able to complete both the VO2peak and 6MAT assessments. Regression analysis yielded the following equation to predict VO2peak from end-stage 6MAT VO2: VO2peak (mL·kg-1·min-1) = 1.501(6MAT VO2) - 0.940. Correlation between measured and predicted VO2peak was excellent (r=0.89). No significant difference was found between measured (17.41±7.44 mL·kg-1·min-1) and predicted (17.42±6.61 mL·kg-1·min-1) VO2peak (p=0.97). When cross-validated with a sample of 10 individuals with SCI, correlation between measured and predicted VO2peak remained high (r=0.89), with no differences between measured (18.81 ± 8.35 mL·kg-1·min-1) and predicted (18.73 ± 7.27 mL·kg-1·min-1) VO2peak (p=0.75). CONCLUSIONS: Results suggest that 6MAT VO2 can be used to predict VO2peak among individuals with chronic SCI. The 6MAT should be used as a clinical tool for assessing aerobic capacity when peak exercise testing is not feasible. Key Words: Arm Ergometry; Peak Oxygen Consumption; Prediction Equation, Submaximal Exercise Test, Spinal Cord Injury
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".