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Record W2013558489 · doi:10.1177/1055329003256653

Successful Techniques for Retaining a Cohort of Infants and Children Born to HIV-Infected Women: the Prospective P2C2 HIV Study

2004· article· en· W2013558489 on OpenAlexaff
Kimberly L. Geromanos, Susan Sunkle, Mary Beth Mauer, Diane Carp, Jessica S. Ancker, Weihong Zhang, Kirk A. Easley, Mark Schluchter, Claudia A. Kozinetz, Robert B. Mellins

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2004
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsColumbia College
FundersNational Center for Research ResourcesNational Heart, Lung, and Blood Institute
KeywordsHuman immunodeficiency virus (HIV)Prospective cohort studyMedicineCohortCohort studyPediatricsVirologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Retaining subjects from disadvantaged populations in long-term studies is necessary to obtain high-quality data. This article presents cumulative retention rates from a 5-year prospective cohort study, the Pediatric Pulmonary and Cardiovascular Complications of Vertically Transmitted HIV Infection study. It also presents results of a cross-sectional qualitative survey about factors that induced caregivers to stay in the study. Although the repeated study visits were long and uncomfortable, cumulative retention among the 298 HIV-infected children was 80%. Incentives considered important by the caregivers included phone contact with nurse coordinators, nurse coordinators accompanying the caregiver and child during visits, phone reminders for appointments, help with scheduling, meals and transportation, access to health care, and relationships with staff. Thus, the high follow-up rate was in part due to nurses' efforts to reduce the study's burden on the families, provide tangible and intangible incentives, and establish personal relationships with families.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.324
Teacher spread0.318 · 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 designObservational
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

Citations8
Published2004
Admission routes1
Has abstractyes

Explore more

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