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Record W2062285874 · doi:10.1097/coh.0b013e3283590617

Optimizing the engagement of care cascade

2012· review· en· W2062285874 on OpenAlexafffund
Mark Hull, Zunyou Wu, Julio Montaner

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2012
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsAIDS VancouverUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institute on Drug AbuseU.S. President’s Emergency Plan for AIDS Relief
KeywordsCartAntiretroviral therapyHuman immunodeficiency virus (HIV)Psychological interventionMedicineIntensive care medicineCascadeClinical trialContinuum of careTransmission (telecommunications)Viral loadComputer scienceHealth careVirologyNursingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: At present, data from mathematical models, ecologic studies and a clinical trial demonstrate that use of combination antiretroviral therapy (cART) can markedly reduce HIV transmission. Expansion of cART uptake (Treatment as Prevention) is a critical component of biomedical interventions to prevent HIV transmission. RECENT FINDINGS: Successful implementation is dependent on identifying undiagnosed individuals, linking and retaining them in care and initiating durable and potent cART regimens. This continuum is encapsulated within the framework of the 'Test and Treat', or 'Seek, Test, Treat and Retain' strategies. Currently only 19-28% of all HIV-infected individuals in the USA are estimated to be virologically suppressed. SUMMARY: Optimizing the engagement of care cascade represents a critical step to maximize the individual and societal impact of cART and therefore deliver on the promise of HIV Treatment as Prevention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.194
GPT teacher head0.461
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations101
Published2012
Admission routes2
Has abstractyes

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