Neuropsychological screening tools in Italian HIV+ patients: a comparison of Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE)
Bibliographic record
Abstract
OBJECTIVE: Despite the progress in HIV treatments, mild forms of cognitive impairment still persist. Brief and sensitive screening tools are needed. We evaluated the accuracy of the Montreal Cognitive Assessment (MoCA) compared to the Mini Mental State Examination (MMSE) to detect cognitive impairment in HIV-infected participants. METHOD: HIV-infected patients were consecutively enrolled during routine outpatient visits at a single institution. The MoCA, the MMSE, and a comprehensive neuropsychological battery were administered. Patients were considered as affected by cognitive impairment if they showed decreased cognitive function in at least two ability domains based on age and education adjusted Italian normative cut-offs. RESULTS: Ninety-three HIV-infected participants (75% males, median age 47, all on antiretroviral therapy; 90% HIV-RNA <50copies/mL, median CD4 644 cells/μL) were enrolled. Thirteen participants (14%) were diagnosed as cognitively compromised via a comprehensive neuropsychological examination. The area under the curve of the adjusted MMSE and MoCA scores to detect cognitive impairment were .51 (95% CI = .31-.72, p = .877) and .70 (95% CI = .53-.86, p = .025), respectively. A MoCA score <22 was able to predict the cognitive impairment with 62% of sensitivity and 76% of specificity. CONCLUSIONS: Our findings suggested that the prognostic performance of the MoCA to detect cognitive impairment among mildly impaired HIV-infected participants was only moderate. Further investigations are needed to identify optimal cognitive tests to screen HIV-infected individuals or to explore whether a combination of cognitive tests might represent a viable alternative to a single screening tool.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".