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Record W2891779850 · doi:10.2196/11207

Adolescent Medicine Trials Network for HIV/AIDS Interventions Data Harmonization: Rationale and Development of Guidelines

2018· article· en· W2891779850 on OpenAlexvenueno aff
Micah McCumber, Demetria Cain, Sara LeGrand, Kenneth H. Mayer, Debra A. Murphy, Matthew A. Psioda, Arlene C. Seña, Tyrel J. Starks, Michael G. Hudgens

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on Minority Health and Health DisparitiesNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of Health
KeywordsPsychological interventionMedicineSocioeconomic statusHarmonizationMental healthFamily medicineAdolescent medicineEnvironmental healthPopulationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program aims to defeat the rising HIV epidemic among adolescents and young adults in the United States. OBJECTIVE: This study aims to optimize cross-study analyses and comparisons of standardized measures (variables) collected in the ATN. METHODS: Guidelines were developed for harmonizing measures to be collected across ATN studies. RESULTS: Eight domains were identified for harmonization-Demographics and Socioeconomic Characteristics, Sexual Behavior and Risk, Substance Use and Abuse, HIV-Positive Cascade, HIV-Negative Cascade, Mental Health, Social Support and Isolation, and Pre-exposure Prophylaxis Cascade. CONCLUSIONS: The collection of selected key measures in a uniform manner across studies facilitates the characterization of participant populations, comparisons between studies, and pooled analysis of data from multiple studies.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Reproducibility · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.024
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.919
GPT teacher head0.742
Teacher spread0.177 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
DomainReproducibility
GenreProtocol · Methods

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

Citations21
Published2018
Admission routes1
Has abstractyes

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