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Record W4311024766 · doi:10.1002/brb3.2837

Factors associated with cognitive impairment at 3, 6, and 12 months after the first stroke among Lebanese survivors

2022· article· en· W4311024766 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBrain and Behavior · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsStroke (engine)Hospital Anxiety and Depression ScaleMedicineAnxietyDepression (economics)CognitionModified Rankin ScaleQuality of life (healthcare)Univariate analysisMontreal Cognitive AssessmentPhysical therapyCognitive impairmentInternal medicinePsychiatryMultivariate analysisIschemic stroke

Abstract

fetched live from OpenAlex

INTRODUCTION: This study aimed to calculate the rate of post-stroke cognitive impairment (PSCI) by evaluating the cognitive domains among Lebanese stroke survivors at 3, 6, and 12 months post-stroke, and to identify the contributing factors including pre- and post-stroke related factors. METHODS: A multicenter longitudinal prospective study was conducted in 10 hospitals from Beirut and Mount Lebanon for a 15-month period. Mini-Mental State Examination (MMSE), modified Rankin Scale (mRS), Short Form Health Survey (SF12), National Institutes of Health Stroke Scale (NIHSS), and Hospital Anxiety and Depression Scale (HADS) were used to assess cognitive function, disability degree, Quality of Life (QoL), stroke severity, and levels of anxiety and depression, respectively. Then, univariate and multivariable analyses were performed to identify the predictors of PSCI. RESULTS: Low MMSE scores were found among survivors during the first 3 months post-stroke (74.8%) of whom 53.7% presented with an MMSE ≤ 17, followed by 46.7% in the 6 months, and 37.6% at 12 months post-stroke. Follow-up comparisons showed a significant increase of MMSE scores over time (p < .001), indicating a 37% improvement of the cognitive function over time. The most affected cognitive domain was the attention and concentration at the three time points. Independent factors that were positively associated with low MMSE scores were as follows: sedentary behavior ≥ 12 h/day (AOR = 3.062, p = .033), involvement of the left hemisphere (AOR = 2.710, p = .006), HADS ≥ 11 (AOR = 2.536, p = .049), and high NIHSS scores (AOR = 3, p = .009). Age was the main predictor in the three time periods (AOR ≈ 3, p < .05). Inversely, female gender (AOR = 0.09, p = .027), high educational level (AOR = 0.2, p < .02), employment post-stroke (AOR = 0.3, p = .023), high Physical Component Summary (PCS) of Quality of Life (QoL) (AOR = 0.8, p < .001), and the use of anti-diabetic treatment post-stroke (AOR = 0.17, p = .016) improved MMSE scores to > 23. CONCLUSION: The risk of PSCI among Lebanese stroke survivors was high especially in the acute phase, depending on various determinants. Health care providers are invited to implement an emergency rehabilitation program for an appropriate successful management of the risk factors in order to reduce stroke burden and to improve overall cognitive performance.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.272
Teacher spread0.251 · 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