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Record W4382895923 · doi:10.52808/bmsa.8e7.632.008

Impacto en el monitoreo de carga viral de VIH en el laboratorio del Hospital Nacional Sergio E. Bernales 2019-2021

2023· article· en· W4382895923 on OpenAlexaff
Flor Eduvines Hilario-Velásquez, Willian Sebastián Flores-Sotelo, Carlos Franco-Del-Carpio

Bibliographic record

VenueBoletín de Malariología y Salud Ambiental · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS oral health manifestations
Canadian institutionsImpact
Fundersnot available
KeywordsViral loadViremiaMedicineHuman immunodeficiency virus (HIV)Antiretroviral therapyInternal medicineImmunology

Abstract

fetched live from OpenAlex

The objective of this study was to determine the impact of monitoring the HIV viral load in the Laboratory of the Sergio E. Bernales National Hospital during the period 2019 to 2021. A descriptive, quantitative, observational, retrospective, comparative, and analytical study was carried out. We worked with 490 patients who had follow-up viral load. The findings revealed a predominance of males with 64% of HIV cases and 36% of females. During the monitoring period, 14.9% of the patients were admitted as new HIV cases, while 8% started antiretroviral treatment (ART). Regarding the viral load results, 14.9% of the patients had a viral load greater than 1000 copies/ml, 5 patients had between 200 and 1000 cp/ml. 70 patients had a viral load greater than 40 to ≤ 1000 cp/mL, 173 patients had a viral load less than 40 cp/mL, and 174 patients had an undetectable viral load. At the end of the monitoring study, a statistical significance of 0.00 and a Pearson correlation coefficient of 0.450 were found, indicating a weak to moderate correlation between the final viral load and the monitoring result. These findings highlight the crucial importance of timely monitoring of viral load in patients with HIV. It was evidenced that 38.3% of the patients who had adequate viral load follow-up achieved viral suppression, 43.5% maintained viral suppression, 14.9% experienced virological failure, and 1% presented resistant viremia.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.322
Teacher spread0.313 · 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; a candidate call from one teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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