Cervicothoracic Mechanical Impairment as Part of Complete Neurological Fall Risk Appraisal
Bibliographic record
Abstract
BACKGROUND: Assessment of individuals at risk for falling entails comprehensive neurological and vestibular examinations. Chronic limitation in cervical mobility reduces gaze accuracy, potentially impairing navigation through complex visual environments. Additionally, humans with scoliosis have altered otolithic vestibular responses, causing imbalance. We sought to determine whether dynamic cervical mobility restrictions or static cervicothoracic impairments are also fall risk factors. METHODS: We examined 435 patients referred for soft-tissue musculoskeletal complaints; 376 met criteria for inclusion (mean age 52; 266 women). Patients were divided into nonfallers, single fallers, and multiple fallers, less or greater than 65 years old. Subject characteristics, dynamic cervical rotations, and static cervicothoracic axial measurements were compared between groups. Fear of falling was evaluated using the Falls Efficacy Scale-International questionnaire. RESULTS: Long-standing cervicothoracic pain and stiffness conferred increased risk of falling. Neck rotation amplitudes decreased with longer duration musculoskeletal symptoms and were significantly more restricted in fallers, doubling the risk of falling and contributing to increased fear of falling. Mid-thoracic scoliosis amplitudes increased over time, but static axial abnormalities were not greater among fallers, although thoracic kyphoscoliosis heightened fear of falling. CONCLUSION: In patients at fall risk, thoracic kyphoscoliosis and dynamic neck movements should be assessed, in addition to standard vestibular and neurological evaluations. Additionally, patients with soft tissue cervicothoracic pain and restricted mobility have increased fall frequency and fear of falling, independent of other fall risk factors and should undergo complete fall risk appraisal.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".