Abstract TMP102: Robotic Assessment Of Proprioceptive Dysfunction In Children With Perinatal Stroke
Bibliographic record
Abstract
Objective: Perinatal stroke causes most hemiplegic cerebral palsy. Sensory dysfunction has been ignored and objective measurement tools are limited. Robotic technology can quantify complex sensory function in adult stroke but has not been applied to kids. Methods: Children from the Alberta Perinatal Stroke Project had MR confirmed unilateral perinatal stroke and upper extremity functional deficit. A bilateral exoskeletal robot (KINARM) capable of testing planar upper limb movements in an augmented reality environment was employed. Primary robotic outcomes were 2 dimensional variability, shift, and contraction/expansion scores of a position-matching task (Figure). Blinded clinical measures of sensory function (touch, proprioception, graphesthesia, stereognosis) were scored. Matched controls (age/gender) were tested. Results: Five children (median 14 yrs, 3 male) with perinatal stroke (3 PVI, 2 arterial) were compared to 7 controls. Stroke children demonstrated marked impairment in position matching including variability (6.48±1.4 vs 3.89±0.7cm, p= 0.001) and shift (5.05±2.2 vs 2.00±1.3cm). Contraction/expansion ratios were also abnormal (0.56±0.27 vs 0.31±0.22; p=0.09). Clinical sensory scores were lower but correlated poorly with robotic measures and motor function. Assessments were well tolerated with no adverse events. Conclusion: Robotic quantification of proprioception is feasible in perinatal stroke. Sensitivity and quantification appear superior to clinical exam. Disordered proprioception is an under-recognized component of disability and a novel therapeutic target.
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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 | 0.001 |
| 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 | 0.000 |
| 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".