Does the Finger-to-Nose Test measure upper limb coordination in chronic stroke?
Bibliographic record
Abstract
BACKGROUND: We aimed to kinematically validate that the time to perform the Finger-to-Nose Test (FNT) assesses coordination by determining its construct, convergent and discriminant validity. METHODS: Experimental, criterion standard study. Both clinical and experimental evaluations were done at a research facility in a rehabilitation hospital. Forty individuals (20 individuals with chronic stroke and 20 healthy, age- and gender-matched individuals) participated.. Both groups performed two blocks of 10 to-and-fro pointing movements (non-dominant/affected arm) between a sagittal target and the nose (ReachIn, ReachOut) at a self-paced speed. Time to perform the test was the main outcome. Kinematics (Optotrak, 100Hz) and clinical impairment/activity levels were evaluated. Spatiotemporal coordination was assessed with slope (IJC) and cross-correlation (LAG) between elbow and shoulder movements. RESULTS: Compared to controls, individuals with stroke (Fugl-Meyer Assessment, FMA-UE: 51.9 ± 13.2; Box & Blocks, BBT: 72.1 ± 26.9%) made more curved endpoint trajectories using less shoulder horizontal-abduction. For construct validity, shoulder range (β = 0.127), LAG (β = 0.855) and IJC (β = -0.191) explained 82% of FNT-time variance for ReachIn and LAG (β = 0.971) explained 94% for ReachOut in patients with stroke. In contrast, only LAG explained 62% (β = 0.790) and 79% (β = 0.889) of variance for ReachIn and ReachOut respectively in controls. For convergent validity, FNT-time correlated with FMA-UE (r = -0.67, p < 0.01), FMA-Arm (r = -0.60, p = 0.005), biceps spasticity (r = 0.39, p < 0.05) and BBT (r = -0.56, p < 0.01). A cut-off time of 10.6 s discriminated between mild and moderate-to-severe impairment (discriminant validity). Each additional second represented 42% odds increase of greater impairment. CONCLUSIONS: For this version of the FNT, the time to perform the test showed construct, convergent and discriminant validity to measure UL coordination in stroke.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".