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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".