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Record W4413139815 · doi:10.1016/s2352-3018(25)00166-3

Research gaps for children who are HIV-exposed but uninfected: outcomes of a research prioritisation workshop

2025· review· en· W4413139815 on OpenAlexaff
Catherine J. Wedderburn, Ceri Evans, Elaine J. Abrams, Alasdair Bamford, Adrie Bekker, Madeleine J. Bunders, Cristina Epalza, Caroline Foster, Lisa Frigati, Tessa Goetghebuer, Grace John‐Stewart, Christian R. Kahlert, Cinta Moraleda, Victor Musiime, Angelina Namiba, Eleni Nastouli, Irene Njuguna, Savita Pahwa, Talía Sainz, Lena Serghides, Mercy Shibemba, Priscilla Ruvimbo Tsondai, Kathleen M. Powis, Claire Thorne, Andrew J. Prendergast

Bibliographic record

VenueThe Lancet HIV · 2025
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineHuman immunodeficiency virus (HIV)Environmental healthFamily medicineHealth equityPublic healthNursing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.128
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.128
Threshold uncertainty score0.675

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1280.142
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0080.009
Open science0.0030.011
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.185
GPT teacher head0.465
Teacher spread0.281 · 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 designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractno

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