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Record W2693646420 · doi:10.1136/bmj.j2989

Most deaths of babies and brain injuries in childbirth are avoidable, says college

2017· article· en· W2693646420 on OpenAlexaboutno aff
Jacqui Wise

Bibliographic record

VenueBMJ · 2017
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsChildbirthMedicineQuarter (Canadian coin)PediatricsFamily medicineMedical emergencyPregnancy

Abstract

fetched live from OpenAlex

Three quarters of deaths of babies or injuries to their brain during childbirth could be avoided with better fetal monitoring and neonatal care, a report from the Royal College of Obstetricians and Gynaecologists has concluded.1 An investigative team conducted more than 2500 expert assessments of local reviews into the care of 1136 babies born in the UK in 2015, of whom 126 were stillborn, 156 died within the first seven days of birth, and 854 had severe brain injury. A quarter of the local reports were branded inadequate as they were not thorough enough to allow a full assessment. The Each Baby Counts report, the findings of …

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)0.001
metaresearch head score (Gemma)0.015
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.006

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 head0.028
GPT teacher head0.354
Teacher spread0.327 · 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
GenreCommentary

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

Citations1
Published2017
Admission routes1
Has abstractyes

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