Gender Differences in Subacute Post-Stroke Patients During Rehabilitation: Functional, Cognitive, and Nutritional Insights
Bibliographic record
Abstract
Background/Objectives: Despite the well-documented gender differences observed during hospitalisation, research in post-stroke recovery remains limited. This study aims to clarify this topic in subacute post-stroke patients undergoing rehabilitation, considering not only functional and cognitive outcomes but also nutritional status and food consumption. Methods: At admission (T0), patients were assessed for demographic, anamnestic, and clinical data and were diagnosed for malnutrition according to the Global Leadership Initiative on Malnutrition (GLIM) criteria. At T0 and after a six-week rehabilitation program (T1), nutritional status was assessed by anthropometric measurements, serum analysis of albumin, glucose, lipidic, metal, and oxidative stress panel, and the calculation of the Geriatric Nutritional Risk Index; food consumption was recorded daily. Functional independence in Activities of Daily Living was measured at both T0 and T1 by the modified Barthel Index (mBI), and cognitive impairment was assessed by the Montreal Cognitive Assessment (MoCA), adjusted for age and education. Results: We enrolled 87 patients (mean age 69 ± 12 years; 42 women and 45 men); of these 52.4% of women were malnourished, compared to 33.3% of men. After rehabilitation (T1), women showed higher oxidative stress (549 ± 143 vs. 491 ± 121 UCARR; p = 0.041) and poorer functional outcomes (55.3 ± 26.1 vs. 67.1 ± 21.8; p = 0.032), despite similar cognitive improvements (19.5 ± 6.4 vs. 21.9 ± 5.2; p = 0.060) compared with men. Conclusions: This study highlights the importance of personalised treatment strategies that account for gender-specific differences to optimise recovery in post-stroke patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".