Increasing the usefulness of tandem walking evaluation.
Bibliographic record
Abstract
OBJECTIVE: Tandem walking testing is a "standard" clinical technique for assessing balance and gait, but it is not a standardized test. We wished to develop a protocol by which we could measure sway during tandem walking at a patient's preferred cadence and at an altered cadence, which might be more challenging. DESIGN: Fifteen patients with vestibular complaints were evaluated with standard tandem walking testing while wearing the Swaystar belt-mounted accelerometer and were also assessed in the same way with tandem walking at two altered cadences. METHODS: We measured tandem walking sway amplitude and sway velocity with eyes open in both pitch and roll planes at a patient's preferred cadence, at 75% of their preferred cadence, and at 125% of their preferred cadence. MAIN OUTCOME MEASURES: We measured total sway amplitude and sway velocity in pitch and roll planes during tandem walking with eyes open while wearing Swaystar to see if there was any increase in sway at nonpreferred cadences. RESULTS: There was no correlation between preferred cadence and the age of the patient. However, there was a significant increase in both sway amplitude and sway velocity at both of the nonpreferred cadences. CONCLUSION: Disruption of a patient's preferred cadence may present an unnatural task, and this challenge to a patient's innate gait may help detect subtle vestibular disease.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".