The Effect of External Power Output and Its Reliability on Propulsion Technique Variables in Wheelchair Users With Spinal Cord Injury
Bibliographic record
Abstract
The purpose of this study was to assess 1) how treadmill slope variance affected external power output (PO) and propulsion technique reliability; and 2) how PO is associated with propulsion technique. Eighteen individuals with spinal cord injury performed two wheelchair treadmill exercise blocks (0% and 1% treadmill slope, standardized velocity) twice on two separate days. PO, velocity, and 14 propulsion technique variables were measured. In a follow-up study, N = 29 performed wheelchair treadmill drag tests. Target and actual slope were documented and PO, intraclass correlation coefficients (ICC) and smallest detectable differences (SDD) were calculated. Within and between visits, the reliability study ICCs were perfect for velocity (1.0), weak for PO (0.33-0.46), and acceptable (>0.70) for five (0% slope) and 10 (1% slope) propulsion technique variables, resulting in SDDs of 35-196%. Measured PO explained 56-90% of the variance in key propulsion technique variables. In the follow-up, PO ICCs were weak (0.43) and SDDs high. Bias between target and actual slope appeared random. In conclusion, PO variability accounts for 50-90% of the variability in propulsion technique variables when speed and wheelchair set-up are held constant. Therefore, small differences in PO between interventions could mask the effect of the interventions on propulsion technique.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".