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Record W2981148441 · doi:10.1093/cid/ciz1015

Ending the Epidemic in America Will Not Happen if the Status Quo Continues: Modeled Projections for Human Immunodeficiency Virus Incidence in 6 US Cities

2019· article· en· W2981148441 on OpenAlexaff
Bohdan Nosyk, Xiao Zang, Emanuel Krebs, Jeong Eun Min, Czarina N. Behrends, Carlos del Rı́o, Julia C. Dombrowski, Daniel J. Feaster, Matthew R. Golden, Brandon D. L. Marshall, Shruti H. Mehta, Lisa R. Metsch, Bruce R. Schackman, Steven Shoptaw, Steffanie A. Strathdee

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsAIDS VancouverSimon Fraser University
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsIncidence (geometry)Status quoContext (archaeology)Human immunodeficiency virus (HIV)DemographyPopulationVirologyGeographyMedicineEnvironmental healthPolitical scienceMathematicsSociology

Abstract

fetched live from OpenAlex

We estimated 10-year (2020-2030) trajectories for human immunodeficiency virus incidence in 6 US cities. Estimated incidence will only decrease in 2 of 6 cities, with the overall population-weighted incidence decreasing 3.1% (95% credible interval [CrI], -1.0% to 8.5%) by 2025, and 4.3% (95% CrI, -2.6% to 12.7%) by 2030 across cities. Targeted, context-specific combination implementation strategies will be necessary to meet the newly established national targets.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.433
Teacher spread0.375 · 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 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

Citations35
Published2019
Admission routes1
Has abstractyes

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