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Record W7135704950

PROSPERO: Interventions to improve birth outcomes of pregnant women living in low- and middle-income countries:a systematic review and network meta-analysis

2018· other· en· W7135704950 on OpenAlexaff
Jay Park, Ofir Harari, Mei Lan  ; id_orcid 0000-0003-1990-7537 Fang, Louis Dron, Richard Lester, Kristian Thorlund, Edward Joseph Mills

Bibliographic record

VenueDiscovery Research Portal (University of Dundee) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsPsychological interventionPregnancyMEDLINESystematic reviewPublic healthHealth care
DOInot available

Abstract

fetched live from OpenAlex

PROSPERO is produced by CRD and funded by the National Institute for Health Research (NIHR)

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 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)0.014
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.056
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.019
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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.

Study designMeta-analysis
DomainMethods
GenreProtocol

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

Citations0
Published2018
Admission routes1
Has abstractyes

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