Bibliographic record
Abstract
Abstract Background Post‐stroke cognitive impairment is a serious complication which could be easily overlooked. Our study aimed to evaluate patients' awareness of post‐stroke cognitive impairment. Methods We included 74 patients (40 women) after acute stroke. Cognitive decline was assessed using the self‐reported questionnaire, the Montreal Cognitive Assessment (MoCA) and the Hachinski Ischaemic Score (HIS). A previously validated cut‐off score of 24/25 [1] was used to determine cognitive impairment. Patients were divided into four groups. Demographic data and MoCA score were compared among four groups using ANOVA. Results The mean age of patients was 68.4 years (SD 14.32), the mean MoCA was 21.8 (SD 5.75), and the mean HIS was 8.1 (SD 2.8). Both self‐reported post‐stroke cognitive impairment incidence and MoCA‐evaluated incidence were 63%. MoCA score significantly correlated with education (r = 0.36; p<0.001) and age (r = ‐0.38; p<0.001), but not with HIS. Thirty‐one patients were aware of their cognitive decline (MoCA 18.2; SD 1), 16 denied cognitive decline despite mean MoCA 20 (SD 1), 16 falsely reported cognitive decline (MoCA 27; SD 0.4), and 11 correctly denied cognitive impairment (MoCA 27, SD 0.4). There were no significant differences between groups in HIS. For self‐reported post‐stroke cognitive impairment, sensitivity was 66%, specificity was 59%, positive predictive value was 66%, and negative predictive value was 59%. The accuracy rate was 57%, and precision 63%. Conclusions our results demonstrate that cognitive impairment is common in patients after acute stroke. However, self‐awareness is low. Therefore, it is necessary to search for post‐stroke cognitive impairment actively, especially in older, lower‐educated patients. Reference [1] Potocnik J, Ovcar Stante K, Rakusa M (2020) The validity of the Montreal cognitive assessment (MoCA) for the screening of vascular cognitive impairment after ischemic stroke. Acta Neurol Belg 120:681‐685. https://doi.org/10.1007/s13760‐020‐01330‐5
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".