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A Multistate Markov Model Based on CD4 Cell Count for HIV/AIDS Patients on Antiretroviral Therapy (ART)

2013· article· en· W2157316029 on OpenAlexvenueno aff
Gurprit Grover, Adesh Kumar Gadpayle, Prafulla Kumar Swain

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAntiretroviral therapyHuman immunodeficiency virus (HIV)MedicineProportional hazards modelMarkov modelTransmission (telecommunications)Antiretroviral treatmentDemographyMarkov chainStatisticsInternal medicineImmunologyViral loadMathematicsComputer science

Abstract

fetched live from OpenAlex

The main purpose of this study is to assess the impact of Antiretroviral Therapy (ART) by using a multistate Markov model to estimate transition intensities and transition probabilities among various states (transient as well as absorbing) of the AIDS patients. A total of 580 AIDS patients were included in this study who are undergoing Antiretroviral Therapy treatment in the ART centre in New Delhi during the period of April 2004 to April 2011. The patients are classified in different states on the basis of their available CD4 cell counts. The authors also estimated the mean sojourn time and total length of stay in each state before absorption, and also examined the effects of explanatory variables (i.e Age, Sex, Mode of transmission) on the rates of transition using Cox’s proportional hazard model.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.426
Teacher spread0.383 · 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 designSimulation or modeling
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

Citations10
Published2013
Admission routes1
Has abstractyes

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