Impact of Neurocognitive Deficits on Viral Load Suppression Among Newly Diagnosed HIV-Infected Patients
Bibliographic record
Abstract
Background. HIV load suppression is an important treatment goal and has been associated with decrease in HIV-related morbidity and mortality as well as reduced rate of HIV transmission. Identifying patients at risk of virological failure is important to better guide resources to help achieve their treatment goals. Neurocognitive deficits are common in newly diagnosed HIV-infected patients and could influence antiretroviral adherence and increase risk of treatment failure. We hypothesized that newly diagnosed HIV-infected patients with baseline neurocognitive deficits are at higher risk of virological failure. Methods. We used the Montreal cognitive assessment (MoCA) score <26, which has been previously validated, to identify patients with neurocognitive deficits. HIV load suppression was defined as undetectable HIV load (less than 48 copies/mL) at 1 year after entry into care. Logistic regression was performed to determine the association of low baseline MoCA score and failure to achieve viral load suppression at one year. Results. Among 138 patients enrolled in the study, 54 (39.1%) patients failed to achieve viral load suppression within 1 year, and 100 (72.5%) patients had baseline MoCA score <26. MoCA score < 26 was significantly associated with a higher risk of virological failure (odds ratio [OR], 3.19; 95% confidence interval [CI], 1.33–7.65). None of the confounding variables were significantly associated with viral load suppression, however variables significantly associated with low MoCA score included higher age (P < 0.01) and presence of depression (P < 0.01). After adjusting for these variables, MoCA score less than 26, was significantly associated with virological failure (OR, 2.7; 95% CI, 1.09–6.69). Conclusion. Baseline neurocognitive deficit as measured by MoCA was associated with a higher risk of treatment failure. A brief clinical test, such as MoCA, can be used to identify patients at risk for treatment failure and could be provided more ancillary support to better achieve viral load suppression. Disclosures. All authors: No reported disclosures.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".