Assessment of cognition as a predictor of prognosis in inpatients with brain damage: A Scoping ReviewProtocol v1
Bibliographic record
Abstract
Objective: The purpose of this study is to determine what cognitive assessments are used to predict or determine outcomes in inpatients with brain damage. Introduction: The prognosis of hospitalized stroke and brain injury patients is related not only to physical function but also to cognition. Global mental functions such as the Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), and Functional Independence Measure (FIM) are commonly used to assess cognition in stroke patients. It is not clear whether these assessments are used because they have better predictive discriminating ability than other assessments. Inclusion criteria: Patients with stroke, traumatic cerebral hemorrhage, or subarachnoid hemorrhage who are hospitalized are included in the study. Eligibility criteria will consist of studies that use cognitive assessments (global mental functions, higher brain function, memory, attention, neglect, apraxia, disorientation, executive function, multitasking, apathy) as exposure or covariates and examine the association between gait, falls, hospital discharge, activities of daily living, and quality of life. The study design will be observational, but case studies (case reports, case series), intervention studies, and systematic reviews will be excluded. In addition to peer-reviewed articles, conference abstracts will be included in the search. Countries and languages are not restricted. Methods: Databases (PubMed, Web of Science, Scopus, CINAHL, Igaku Chuo Zasshi) will be used for searching. NPO Japan Medical Abstracts Society operates Igaku Chuo Zasshi. Results will be tabulated by country, publication type, study design, type of analysis (univariate or multivariate), names of cognitive assessments and when to assess them, and type and timing of theoutcome. In addition, we will also add whether there are exclusion criteria due to cognitive impairment such as aphasia and whether studies are comparing the MMSE, MoCA, and FIM-Cog with other cognitive assessments.
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".