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Record W4409318530 · doi:10.1016/s0140-6736(25)00151-5

The 2025 report of the Lancet Countdown to 2030 for women's, children's, and adolescents' health: tracking progress on health and nutrition

2025· review· en· W4409318530 on OpenAlexaff
Agbessi Amouzou, Aluísio J. D. Barros, Jennifer Requejo, Cheikh Fayé, Nadia Akseer, Eran Bendavid, Cauane Blumenberg, Josephine Borghi, Sama El Baz, Frederik Federspiel, Leonardo Z. Ferreira, Elizabeth Hazel, Sam Heft‐Neal, Franciele Hellwig, Li Liu, Melinda Munos, Catherine Pitt, Jeremy Shiffman, Yvonne Tam, Neff Walker, Pierre Akilimali, Leontine Alkema, Peter Binyaruka, Zulfiqar A Bhutta, Andrea Katryn Blanchard, Hannah Blencowe, Ellen Bradley, Nouria Brikci, Beatriz Caicedo-Velásquez, Anthony Costello, Winfred Dotse‐Gborgbortsi, Shams El Arifeen, Majid Ezzati, Lynn P. Freedman, Michel Guillot, Claudia Hanson, Rebecca Heidkamp, Luis Huicho, Chimaraoke Izugbara, Safia S Jiwani, Caroline W. Kabiru, Helen Kiarie, Mary Kinney, Fati Kirakoya‐Samadoulougou, Joy E Lawn, Nyovani Madise, Gouda Roland Mesmer Mady, Bruno Masquelier, Dessalegn Y. Melesse, Kristine Nilsen, Jamie Perin, Usha Ram, Marina Romanello, Ghada E. Saad, Sudha Sharma, Estelle Sidze, Paul Spiegel, Hannah Tappis, Andrew J. Tatem, Marleen Temmerman, César G. Victora, Francisco Villavicencio, Yohannes Dibaba Wado, Peter Waiswa, Jon Wakefield, Shelley Walton, Danzhen You, Mickey Chopra, Robert E. Black, Ties Boerma

Bibliographic record

VenueThe Lancet · 2025
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of ManitobaManitoba Health
FundersWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsCountdownTracking (education)Environmental healthMedicineGerontologyPediatricsPsychologyEngineering

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0150.007

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 head0.052
GPT teacher head0.390
Teacher spread0.339 · 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 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

Citations46
Published2025
Admission routes1
Has abstractno

Explore more

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