Recognizing Cognitive and Psychiatric Changes in the Post‐Highly Active Antiretroviral Therapy Era
Bibliographic record
Abstract
Amid numerous complications that plague the health and quality of life of people living with HIV, neurocognitive and psychiatric illnesses pose unique challenges. While there remains uncertainty with respect to the pathophysiology surrounding these disorders, their adverse implications are increasingly recognized. Left undetected, they have the potential to significantly impact patient well being, adherence to antiretroviral treatment and overall health outcomes. As such, early identification of HIV-associated neurocognitive disorders (HAND) and psychiatric illnesses will be paramount in the proactive management of affected patients. The present review focuses on strategies to ensure optimal screening and detection of HAND, depression and substance abuse in routine practice. For each topic, currently available screening methods are discussed. These include identification of risk factors, recognition of relevant symptomatology and an update on validated screening tools that can be efficiently implemented in the clinical setting. Specifically addressed in the present review are the International HIV Dementia Scale, a novel screening equation and algorithm for HAND, as well as brief, validated, verbal questionnaires for detection of depression and substance abuse. Adequate understanding and usage of these screening mechanisms can ensure effective use of resources by distinguishing patients who require referral for more extensive diagnostic procedures from those who likely do not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".