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Record W1985781493 · doi:10.1016/s0140-6736(13)60686-8

Moving beyond essential interventions for reduction of maternal mortality (the WHO Multicountry Survey on Maternal and Newborn Health): a cross-sectional study

2013· article· en· W1985781493 on OpenAlexaff
João Paulo Souza, A. Metin Gülmezog̈lu, Joshua P. Vogel, Guillermo Carroli, Pisake Lumbiganon, Zahida Qureshi, María José Costa, Bukola Fawole, Yvonne Mugerwa, Nafiou Idi, Isilda Neves, Jean-José Wolomby-Molondo, Hoang Thi Bang, Kannitha Cheang, Kang Chuyun, Kapila Jayaratne, Chandani Anoma Jayathilaka, Syeda Batool Mazhar, Rintaro Mori, Mir Lais Mustafa, Laxmi Pathak, Deepthi Perera, Tung Rathavy, Zenaida Dy Recidoro, Malabika Roy, P Ruyan, Surasak Taneepanichsku, Nguyen Viet Tien, Togoobaatar Ganchimeg, Mira Wehbe, Buyanjargal Yadamsuren, Yan Wang, Khalid Yunis, Vicente Bataglia, José Guilherme Cecatti, Bernardo Hernández, Juan Manuel Nardin, Alberto Cerezo-Narváez, Eduardo Ortiz‐Panozo, Ricardo Pérez‐Cuevas, Eliette Valladares, Nelly Zavaleta, Anthony Armson, Caroline A Crowther, Carol J. Hogue, Gunilla Lindmark, Suneeta Mittal, Robert Pattinson, Mary Ellen Stanton, Liana Campodónico, Cristina Cuesta, Daniel Giordano, Nirun Intarut, Malinee Laopaiboon, Rajiv Bahl, José Martines, Matthews Mathai, Mario Merialdi, Lale Say

Bibliographic record

VenueThe Lancet · 2013
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePsychological interventionCross-sectional studyEnvironmental healthMaternal healthHealth servicesPopulationPsychiatry

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
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 head0.091
GPT teacher head0.412
Teacher spread0.322 · 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 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

Citations761
Published2013
Admission routes1
Has abstractno

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