PSDRS, BDI, MoCA and MMSE as screening tools for the evaluation of mood and cognitive functions in patients at the early stage of cerebral stroke.
Bibliographic record
Abstract
Introduction Aim of the study: The evaluation of the usefulness of the PSDRS in detecting affective disorders. Examination of the correlation of depressed mood states with cognitive disorders in patients at an early stage of cerebral stroke. Attempt at a comparison of the effectiveness of detecting depressive and cognitive disorders with the application of selected clinical scales. Material and Methods The examination included 43 patients within the first week after cerebral stroke. It was carried out with the application of two screening scales: Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA) and two scales for the evaluation of the degree of depressiveness: Post-stroke Depression Scale (PSDRS) and Beck Depression Inventory (BDI). Results A significant, negative correlation of the results of the PSDRS and MoCA scales was shown. Depressed moods in patients after cerebral stroke in a significant way statistically correlated with the disorders in the selected cognitive skills: visual and spatial functions, memory, attention functions and abstracting ability. Conclusions The PSDRS and MoCA scales proved to be more effective tools of the evaluation of depressive and cognitive disorders in patients at an early stage after cerebral stroke, than it was observed in the case of conventionally applied MMSE and BDI scales. The examination results additionally prove a significant dependence between mood and the efficiency of cognitive functions in this group of patients. With the weakening of cognitive functioning, also the patients’ mood became depressed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".