A Mobile App to Screen for Neurocognitive Impairment: Preliminary Validation of NeuroScreen Among HIV-Infected South African Adults
Bibliographic record
Abstract
BACKGROUND: Neurocognitive impairment (NCI) is one of the most common complications of HIV infection, and has serious medical and functional consequences. South Africa has 7 million people living with HIV (PLHIV) with up to three-quarters of antiretroviral therapy (ART)-naïve individuals having NCI. South Africa's health system struggles to meet the care needs of its millions of PLHIV; screening for NCI is typically neglected due to limited clinical staff trained to administer, score, and interpret neuropsychological tests, as well as long test batteries and limited screening tools for South African populations. Without accurate, clinically useful, and relatively brief NCI screening tests that can be administered by all levels of clinical staff, critical opportunities to provide psychoeducation, behavioral planning, additional ART adherence support, and adjuvant therapies for NCI (when they become available) are missed. To address these challenges and gap in care, we developed an mHealth app screening tool, NeuroScreen, to detect NCI that can be administered by all levels of clinical staff, including lay health workers. OBJECTIVE: The purpose of this study was to examine sensitivity and specificity of an adapted version of NeuroScreen to detect NCI (as determined by a gold standard neuropsychological test battery administered by a trained research psychometrist) among HIV-infected South Africans when administered by a lay health worker. METHODS: A total of 102 HIV-infected black South African adults who had initiated ART at least 12 months prior were administered NeuroScreen and a gold standard neuropsychological test battery in the participants' choice of language (ie, English or isiXhosa). Three composite z scores were calculated for NeuroScreen: (1) sum of all individual test scores, (2) sum of all individual test scores and error scores from four tests, and (3) sum of four tests (abbreviated version). Global deficit scores were calculated for the gold standard battery where a score of 0.5 or greater indicated the presence of NCI. RESULTS: The mean age of participants was 33.31 (SD 7.46) years, most (59.8%, 61/102) had at least 12 years of education, and 81.4% (83/102) of the sample was female. Gold standard test battery results indicated that 26.5% (27/102) of the sample had NCI. Sensitivity and specificity of age-, education-, and sex-adjusted NeuroScreen scores were 81.48% and 74.67% for composite score 1, 81.48% and 81.33% for composite score 2, and 92.59% and 70.67% for composite score 3, respectively. CONCLUSIONS: NeuroScreen, a highly automated, easy-to-use, tablet-based screening test to detect NCI among English- and isiXhosa-speaking South African HIV patients demonstrated robust sensitivity and specificity to detect NCI when administered by lay health workers. NeuroScreen could help make screening for NCI more feasible. However, additional research is needed with larger samples and normative test performance data are needed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".