Evaluating Intrinsic Fall Risk Factors After Incomplete Spinal Cord Injury: Distinguishing Fallers From Nonfallers
Bibliographic record
Abstract
OBJECTIVE: To determine whether performance on measures of lower extremity muscle strength, sensory function, postural control, gait speed, and balance self-efficacy could distinguish fallers from nonfallers among ambulatory individuals with spinal cord injury or disease (SCI/D). DESIGN: Prospective cohort study. SETTING: Community. PARTICIPANTS: Individuals (N=26; 6 female, aged 58.9±18.2y) with motor incomplete SCI/D (American Spinal Injury Association Impairment Scale rating C [n=5] or D [n=21]) participated. Participants were 7.5±9.1 years post injury. Seventeen participants experienced traumatic causes of spinal cord injury. MAIN OUTCOME MEASURES: Participants completed laboratory-based and clinical measures of postural control, gait speed, balance self-efficacy, and lower extremity strength, as well as proprioception and cutaneous pressure sensitivity. Participants were then followed for up to 1 year to track falls using a survey. The survey queried the circumstances and consequences of each fall. If a participant's number of falls equaled or exceeded the median number of falls experience by all participants, they were classified a faller. RESULTS: <.05): measures of lower extremity strength, cutaneous pressure sensitivity, walking speed, and center of pressure velocity in the mediolateral direction. CONCLUSIONS: There are laboratory-based and clinical measures that can prospectively distinguish fallers from nonfallers among ambulatory individuals with spinal cord injury. These findings may assist clinicians when evaluating their patients' fall risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".