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Record W4382362342 · doi:10.37766/inplasy2023.6.0086

Comprehending the Impacts of Enhanced Obstetrical Surveillance Systems Globally on Maternal Morbidity and Mortality: A Living Systematic Review

2023· report· en· W4382362342 on OpenAlexaff
Bronte K. Johnston, Logan C. Barr, Michelle Hwang, Rohan D’Souza

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineChildbirthPregnancyMaternal deathObstetricsPediatricsPopulation

Abstract

fetched live from OpenAlex

Dissemination plansThe results of this systematic review will be published in an open access academic journal with a global readership, and that supports the publication of living systematic reviews.They will also be presented at an international conference of Obstetrics & Gynecology.Summaries and editorials will be published in the World Health Organization Bulletin and journals with a global health and public health focus.In addition, findings will be posted on the INOSS website and on websites of all INOSS member countries.

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)0.020
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.096
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0130.011
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.138
GPT teacher head0.411
Teacher spread0.273 · 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 designSystematic review
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

Citations0
Published2023
Admission routes1
Has abstractyes

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