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Record W2344539872 · doi:10.1093/cid/ciw157

Safe Reduction in CD4 Cell Count Monitoring in Stable, Virally Suppressed Patients With HIV Infection or HIV/Hepatitis C Virus Coinfection

2016· article· en· W2344539872 on OpenAlexaff
David Nicolás, Anna Esteve‐Codina, Anna Cuadros, Colin Campbell, Cristina Tural, Daniel Podzamczer, Javier Murillas, Francisco Homar, Ferrán Segura, Lluís Force, Josep Vilaró, Àngels Masabeu, Jordi Mercadal, Alexandra Montoliu, Elena Ferrer, Melcior Riera, Juan Ambrosioni, Gemma Navarro, Christian Manzardo, Bonaventura Clotet, Josep M. Gatell, Jordi Casabona, José M. Miró

Bibliographic record

VenueClinical Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsVictoria General Hospital
FundersUniversitat de BarcelonaFundación Para La Innovación Y La Prospectiva En Salud En EspañaGeneralitat de CatalunyaStyrelsen för Internationellt Utvecklingssamarbete
KeywordsCoinfectionMedicineHepatitis C virusInternal medicineCohortImmunologyPopulationGastroenterologyHuman immunodeficiency virus (HIV)VirologyVirus

Abstract

fetched live from OpenAlex

BACKGROUND: It has been suggested that routine CD4 cell count monitoring in human immunodeficiency virus (HIV)-monoinfected patients with suppressed viral loads and CD4 cell counts >300 cell/μL could be reduced to annual. HIV/hepatitis C virus (HCV) coinfection is frequent, but evidence supporting similar reductions in CD4 cell count monitoring is lacking for this population. We determined whether CD4 cell count monitoring could be reduced in monoinfected and coinfected patients by estimating the probability of maintaining CD4 cell counts ≥200 cells/µL during continuous HIV suppression. METHODS: The PISCIS Cohort study included data from 14 539 patients aged ≥16 years from 10 hospitals in Catalonia and 2 in the Balearic Islands (Spain) since January 1998. All patients who had at least one period of 6 months of continuous HIV suppression were included in this analysis. Cumulative probabilities with 95% confidence intervals were calculated using the Kaplan-Meier estimator stratified by the initial CD4 cell count at the period of continuous suppression initiation. RESULTS: A total of 8695 patients were included. CD4 cell counts fell to <200 cells/µL in 7.4% patients, and the proportion was lower in patients with an initial count >350 cells/µL (1.8%) and higher in those with an initial count of 200-249 cells/µL (23.1%). CD4 cell counts fell to <200 cells/µL in 5.7% of monoinfected and 11.1% of coinfected patients. Of monoinfected patients with an initial CD4 cell count of 300-349 cells/µL, 95.6% maintained counts ≥200 cells/µL. In the coinfected group with the same initial count, this rate was lower, but 97.6% of coinfected patients with initial counts >350 cells/µL maintained counts ≥200 cells/µL. CONCLUSIONS: From our data, it can be inferred that CD4 cell count monitoring can be safely performed annually in HIV-monoinfected patients with CD4 cell counts >300 cells/µL and HIV/HCV-coinfected patients with counts >350 cells/µL.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.344
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2016
Admission routes1
Has abstractyes

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