Characterizing Postural Stability in a Quasi-Static Sitting Position among Individuals with Sensorimotor Impairments Following Spinal Cord Injury
Bibliographic record
Abstract
The objective of this study was to determine a minimum data set of postural measures to characterize seated stability in individuals with spinal cord injury (SCI) by computing 39 Center-Of-Pressure (COP) measures routinely investigated in standing posture.Two short-sitting positions on an instrumented seat with the feet resting on force plates were compared between 14 individuals with SCI and 14 healthy controls: 1) with both hands on their thighs and 2) with both upper extremities flexed at 70 o and abducted at 45 o .The correlations between all COP measures for the resultant, anteroposterior and mediolateral components were also computed.Differences in seated stability were observed between individuals with SCI and healthy controls, irrespective of the tasks.More precisely, the bilateral hand support was confirmed to be an effective strategy to compensate for anterior instability in individuals with SCI.As anticipated, time domain distance and frequency domain measures revealed complementary information.Distance and area COP measures were highly correlated with each other (i.e., redundant information) but were not correlated with frequency and hybrid measures.For both groups (between-task comparisons), the most discriminative uncorrelated measures were related to frequency parameters (i.e., independent information).Overall, our analyses revealed that a minimal data set of postural measures should include mean distance, mean velocity, centroidal frequency, median power frequency and frequency dispersion.These measures should be reported for all directional components whenever applicable, as both anteroposterior and mediolateral activities independently contribute to the resultant COP outcome measures.
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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 | 0.002 |
| 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 | 0.000 | 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".