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Record W2901553165 · doi:10.1097/qad.0000000000002080

Eliminating perinatal HIV in the United States

2018· review· en· W2901553165 on OpenAlexaff
Devasena Gnanashanmugam, Natella Rakhmanina, Keith W. Crawford, Steven R. Nesheim, Theodore Ruel, Guthrie S. Birkhead, Rana Chakraborty, Robert M. Lawrence, Patrick Jean‐Philippe, Lakshmi Jayashankar, Ashley Hoover, Anne Statton, Patricia D’Souza, Joseph Fitzgibbon, Rohan Hazra, Barbara Warren, S. Smith, Elaine J. Abrams

Bibliographic record

VenueAIDS · 2018
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsTheratechnologies (Canada)
FundersNational Institutes of Health
KeywordsHuman immunodeficiency virus (HIV)Transmission (telecommunications)MedicinePregnancyPerinatal mortalityEnvironmental healthPediatricsFamily medicineFetusComputer science

Abstract

fetched live from OpenAlex

: In 2015, only 53 infants born in the United States acquired HIV - the lowest recorded number of perinatal HIV infections. Recognizing this significant achievement, we must acknowledge that the United States has not yet reached the goal of eliminating perinatal HIV transmission. This analysis describes different approaches to perinatal HIV preventive services among five states and the District of Columbia as case studies. Continuous focus on improving identification, surveillance and prevention of HIV infection in pregnant women and their infants is necessary to reach the goal of eliminating perinatal HIV transmission in the United States.

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 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 score0.920
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.422
Teacher spread0.346 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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