Utility of the Montreal Cognitive Assessment as a Screening Test for Neurocognitive Dysfunction in Adults with Sickle Cell Disease
Bibliographic record
Abstract
OBJECTIVES: Neurocognitive dysfunction is an important complication of sickle cell disease (SCD), but little is published on the utility of screening tests for cognitive impairment in people with the disease. The purpose of this study was to evaluate the Montreal Cognitive Assessment (MoCA) as a screening tool and identify predictors of MoCA performance in adults with sickle cell disease. METHODS: We conducted a retrospective, cross-sectional study of the first 100 adult patients with SCD who completed the MoCA as part of routine clinical care at the Johns Hopkins Sickle Cell Center for Adults. We abstracted demographic, laboratory, and clinical data from each participant's electronic medical record up to the date that the MoCA was administered. The factorial validity of each MoCA domain was analyzed using standard psychometric statistics. We evaluated the abstracted data for associations with the composite MoCA score and looked for independent predictors of performance using multivariable regressions. RESULTS: Components of the MoCA performed well in psychometric analyses and identified deficits in executive function that were described in other studies. Forty-six percent of participants fell below the cutoff for mild cognitive impairment. Increased education was an independent predictor of increased MoCA score (3.1, 95% confidence interval [CI] 1.5-4.7), whereas cerebrovascular accidents and chronic kidney disease were independent predictors of decreased score (-3.3, 95% CI -5.7 to -0.97 and -3.2, 95% CI -6.2 to -0.11, respectively). When analysis was restricted to patients with SCA, increased education (3.7, 95% CI 2.2-5.2) and a history of hydroxyurea therapy (2.0, 95% CI -0.022 to 4.0) were independent predictors of a higher score, whereas chronic kidney disease (-3.3, 95% CI -6.4 to -0.24) and increased aspartate transaminase (-0.045, 95% CI -0.089 to -0.0010) were independent predictors of a decreased score. CONCLUSIONS: The MoCA showed promise by identifying important cognitive deficits and associations with chronic complications and therapy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".