Bibliographic record
Abstract
<div> Introduction Stroke survivors develop cognitive impairment, which significantly impacts their quality of life, their families, and the community as a whole but not given attention. This study aims to determine the incidence and predictors of post-stroke cognitive impairment (PSCI) among adult stroke patients admitted to a tertiary hospital in Dodoma, Tanzania. Methodology A prospective cohort study was conducted at tertiary hospitals in the Dodoma region, central Tanzania. A sample size of 158 participants with the first stroke confirmed by CT/MRI brain aged ≥ 18 years met the criteria. At baseline, social-demographic, cardiovascular risks and stroke characteristics were acquired, and then at 30 days, participants were evaluated for cognitive functioning using Montreal Cognitive Assessment (MoCA). Key confounders for cognitive impairment, such as depression and apathy, were evaluated using the Personal Health Questionnaire (PHQ-9) and Apathy Evaluation Scale (AES), respectively. Descriptive statistics were used to summarise data; continuous data were reported as Mean (SD) or Median (IQR), and categorical data were summarised using proportions and frequencies. Univariate and multivariable logistic regression analysis was used to determine predictors of PSCI. Results The median age of the 158 participants was 58.7 years; 57.6% of them were female, and 80.4% of them met the required criteria for post-stroke cognitive impairment. After multivariable logistic regression, left hemisphere stroke (AOR: 5.798, CI: 1.030–32.623, <i>p</i> = 0.046), a unit cm<sup>3</sup> increase in infarct volume (AOR: 1.064, 95% CI: 1.018–1.113, <i>p</i> = 0.007), and apathy symptoms (AOR: 12.259, CI: 1.112–89.173, <i>p</i> = 0.041) had a significant association with PSCI. Conclusion The study revealed a significant prevalence of PSCI; early intervention targeting stroke survivors at risk may improve their outcomes. Future research in the field will serve to dictate policies and initiatives. </div>
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 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.639 | 0.284 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".