Abstract TP158: Neural Function and Injury Combined Best Predict Behavioral Gains During Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
Background: A better understanding of the mechanisms of recovery during rehabilitation could inform treatment decision-making. We tested two hypotheses: [1] a combination of neural function and injury measures is better than either measure alone for predicting motor gains during inpatient rehabilitation facility (IRF) admission; and [2] performance of prediction measures varies according to severity of baseline impairment. Methods: Fifteen patients with subacute stroke (56±12 yr, 16 days post-stroke) admitted to an IRF underwent EEG [3-min, resting-state, dense-array (256-lead)] and MRI [anatomical and diffusion tensor] at IRF admission; and serial behavioral testing. Neural function was assessed using EEG measures of coherence and power from electrodes overlying ipsilesional (M1 i ) and contralesional (M1 c ) primary motor cortex, in the Delta (1-3 Hz) and high Beta (20-30 Hz) frequency bands. Neural injury was assessed as integrity of white matter in corpus callosum (CC). Change in arm Fugl-Meyer (FM) and Functional Independent Measurement motor (FIM-m) scores served as primary and secondary behavioral recovery metrics, respectively. Results: In subjects with moderate or severe impairment (FM <55, N=11), neither neural function (M1 i -M1 c Delta coherence) nor neural injury (CC integrity) alone significantly predicted FIM-m score change. However, when combined into a single model, these measures did significantly predicted FIM-m score change (R 2 = 0.85, p=0.024); note that baseline behavior was not a significant predictor. An identical neural injury+function model approached significance at predicting FM score change (R 2 = 0.72 p=0.08). These models failed, however, when applied to all 15 patients (R 2 = 0.06, p=0.81). Conclusions: Results thus far in this ongoing study suggest that recovery during inpatient rehabilitation is best predicted by combining neural function and injury measures, and not by behavioral assessments. Performance of recovery predictors varies according to severity of baseline deficits, as adding mild strokes to moderate/severe strokes increased the sample size but diluted the model. These findings could potentially inform patient selection, treatment decisions, and discharge planning in an IRF setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".