Dizziness Handicap Inventory Score Is Highly Correlated With Markers of Gait Disturbance
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between Dizziness Handicap Inventory-Screening version (DHI-S) score and spatiotemporal gait parameters using SoleSound, a newly developed, inexpensive, portable footwear-based gait analysis system. STUDY DESIGN: Cross-sectional. PATIENTS: One hundred eighteen patients recruited from otology clinic. INTERVENTION(S): Subjects completed the DHI-S survey and four uninterrupted walking laps wearing SoleSound instrumented footwear on a hard, flat surface for 100 m. MAIN OUTCOME MEASURE(S): For each subject, mean and coefficient of variation (CV) of stride length, cadence, walking speed, foot-ground clearance, double-support time, swing period, and stance-to-swing were computed by considering 40 strides of steady-state walking within each lap. Linear regression models were employed to study correlations between these variables and DHI-S scores after adjusting for age, sex, and race/ethnicity. RESULTS: Patients with higher DHI-S score took shorter steps and less steps per minute (-0.017 m and -1.1 steps/min per every four-point increase in DHI-S score, p < 0.05) than patients with a lower DHI-S score, with slower walking speed (-0.025 m/s per every four-point increase in DHI-S score, p < 0.01). Additionally, patients with higher DHI-S scores showed larger variability in all analyzed temporal parameters (+0.1% for CV of cadence, +0.5% for CV of double support period, +0.2% for CV of swing period, and +0.4% for CV of stance-to-swing, per every four-point increase in DHI-S score, p < 0.01). CONCLUSION: SoleSound was effective in measuring a wide range of gait parameters. Patients' self-perception of vestibular handicap, as assessed with DHI-S, is associated with deterioration in measurable gait parameters independent of age.
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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 | 0.004 |
| 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 | 0.000 |
| 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".