VARIABILITY MEASURES UNLOCK THE CLINICAL UTILITY OF GAITRITE ASSESSMENT FOR PREDICTING MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
This study aimed to establish criterion validity of normative regression GAITRite values for discriminating between amnestic Mild Cognitive Impairment (a-MCI) and cognitively intact controls using a reference sample (N=312, age 69 - 95) of healthy participants from the Victoria Longitudinal Study. Regression norming was used to derive weighted equations which were deployed in a target sample of 34 older adults (a-MCI=12, controls=22, 70–85 years of age). The difference between observed and predicted scores was derived, standardized as T-scores, and then used in logistic regression analyses to classify aMCI and healthy controls. The standardized difference scores for velocity, stride time variability and support base variability while under cognitive load (counting backward by 7’s) yielded a classification accuracy of 93.2% with specificity of 96.9% and sensitivity of 83.3%. This is the first evidence of clinically meaningful classification of aMCI using regression norming values and variability indicators derived from the GAITRite system.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".