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Record W4210588300 · doi:10.21203/rs.3.rs-29666/v1

Prevalence and predictors of HIV-associated Neurocognitive Disorder in Ethiopians

2020· preprint· en· W4210588300 on OpenAlexaff
Salahuddin Mohammed, Md Dilshad Manzar, Hamid Yimam Hassen, Mihretu Ashuro, Aleem Unissa, Mohammed Aziz Mohammed, Unaise Abdul Hameed, D. Warren Spence, Seithikurippu R. Pandi‐Perumal

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsNoNO (Canada)
Fundersnot available
KeywordsNeurocognitivePsychosocialMedicineLogistic regressionDementiaDiseaseHuman immunodeficiency virus (HIV)Depression (economics)PsychiatryGerontologyFamily medicineInternal medicineCognition

Abstract

fetched live from OpenAlex

Abstract Background Modern antiretroviral therapy has extended the life expectancies of people living with HIV; the prevention and treatment of their associated neurocognitive decline has remained a challenge. Consequently, it is desirable to investigate the prevalence and predictors of HIV-associated Neurocognitive Disorder (HAND) to help in targeted screening and disease prevention. Methods: Two hundred and forty-four people living with HIV were interviewed in a study using a cross-sectional design and International HIV Dementia scale (IHDS). Additionally, the sociodemographic, clinical and psychosocial characteristics of the patients were recorded. Chi-square and binary logistic regression analysis were used to determine the level of significance among the independent risk factors and probable HAND. Results The point prevalence of HAND was found to be 39.3%. Participants’ characteristics of being older than 40 years (AOR = 2.81 (95% CI; 1.11–7.15)), having a history of recreational drug use (AOR = 13.67 (95% CI; 6.42–29.13)), and being non-compliant with prescribed medications (AOR = 2.99 (95% CI; 1.01–8.87)) were independent risk factors for HAND. Conclusion The identification of predictors, some of which may be more closely related to the Ethiopian people living with HIV, may help in targeted screening of vulnerable groups during cART follow-up visits. This may greatly help in strategizing and implementation of the prevention program, more so, because: (i) HAND is an asymptomatic condition for considerable durations, and (ii) clinical trials of HAND therapies have been unsuccessful.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.005
Research integrity0.0020.012
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.063
GPT teacher head0.380
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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