MétaCan
Menu
← Back to cohort
Record W3176402192 · doi:10.1161/str.51.suppl_1.tp156

Abstract TP156: NIH Stroke Scale at Discharge as a Predictor for Return to Work Status After Mild Stroke

2020· article· en· W3176402192 on OpenAlexaboutno aff
Susan Taboada, Caroline Wisialowski, Jennifer Blum, Sarah J. Clark, Ilene Staff, Amre Nouh

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentStroke (engine)Depression (economics)Physical therapyLogistic regressionFunctional Independence MeasureRehabilitationCognitive impairmentInternal medicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.016
GPT teacher head0.274
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueStroke→Same topicStroke Rehabilitation and Recovery→French-language works237,207→