Montreal Cognitive Assessment for Evaluating Cognitive Impairment in Subarachnoid Hemorrhage: A Systematic Review
Bibliographic record
Abstract
Subarachnoid hemorrhage (SAH) is a severe condition with high mortality and extensive long-term morbidity. Although research has focused mainly on physical signs and disability for decades, in recent years, it has been increasingly recognized that cognitive and psychological impairments may be present in many patients with SAH, negatively impacting their quality of life. We performed a systematic review aiming to provide a comprehensive report on the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) test for evaluating the presence of cognitive impairment in patients with SAH. Using appropriate search terms, we searched five databases (PubMed, Scopus, PsychINFO, Web of Sciences, and Latin American and Caribbean Health Sciences Literature) up to January 2022. Two cross-sectional studies investigated the accuracy of MoCA in SAH patients in the subacute and chronic phase. We appraised the quality of the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. The MoCA test provides information about general cognitive functioning disturbances. However, a lower threshold than the original cutoff might be needed as it improves diagnostic accuracy, lowering the false positive rates. Further research is necessary for an evidence-based decision to use the MoCA in SAH 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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".