Abstract TP156: NIH Stroke Scale at Discharge as a Predictor for Return to Work Status After Mild Stroke
Bibliographic record
Abstract
Background: A significant proportion of patients are unable to return to work (RTW) post stroke. While post-stroke depression and fatigue have been linked to patients’ RTW status, the role of discharge NIHSS has not been studied. Objective: To evaluate role of stroke severity, depression, fatigue, and cognitive impairment on patients’ ability to RTW. Methods: A retrospective study was conducted using a survey completed by a convenience sample of patients during follow-up in stroke clinic. The survey included PHQ-9, Fatigue Assessment Scale (FAS), and the Montreal Cognitive Assessment (MoCA). Demographic, work status, and clinical data (discharge NIHSS, mRS, medical history) were also collected. NIHSS was evaluated both continuously and dichotomized ( < 1, > 1). Patients who did and did not RTW were compared using chi square tests of proportions and Wilcox Ranked Sum tests; independence of factors was explored using logistic regression predicting RTW. Results: Out of 135 patients surveyed, 41% (N=56) reported employment at the time of their stroke. Of those, a significant percentage of patients were unable to RTW post stroke (57.1%); 39.3% (N=22) were unable to RTW due to physical limitations. Further analysis revealed patients who did not RTW were more likely to suffer from fatigue (p=0.026), have higher rates of cognitive impairment (p=0.027) and a higher NIHSS at discharge (p<0.001). Very low NIHSS was a very strong RTW predictor as patients with an NIHSS ≤ 1 at discharge were 15 times more likely to RTW than patients with a higher NIHSS (p=.001). Patients who worked in professional, managerial, or artistic occupations pre-stroke were more likely to return to work than those in public service, skilled or unskilled labor occupations (p=0.023). In multivariate analyses, fatigue, cognitive impairment and depression were no longer significant when NIHSS at discharge was a covariate. Type of occupation was independent of NIHSS. Conclusions: For patients with mild stroke, NIHSS at discharge indicating minimal to no disability is a strong independent predictor for RTW status. For patients with greater deficit, depression, fatigue and cognitive impairment could play a greater role; additional studies of patients with greater variety of stroke severity would be needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".