MétaCan
Menu
Back to cohort

Obstetric Admissions to Intensive Care Units in Australia and New Zealand: A Registry-based Cohort Study

2021· article· en· W3198106167 on OpenAlexaboutno aff
Matthew J. Maiden, Mark Finnis, Graeme Duke, Emily Y-S Huning, Tim Crozier, N. P. K. Khan Nguyen, V. K. Biradar, Caitlin McArthur, D. Pilcherd

Bibliographic record

VenueObstetric Anesthesia Digest · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEpidemiologyCritically illIntensive care unitIntensive careCohort studyCohortEmergency medicineMedical emergencyIntensive care medicine

Abstract

fetched live from OpenAlex

( BJOG . 2020;127:1558–1567) Critically ill, obstetric patients have unique characteristics and despite risk for complications, this patient population is often excluded from research. While there have been single-center and regional studies as well as national reports from Canada, France, The Netherlands and the UK, the epidemiology of obstetric patients admitted to the intensive care unit (ICU) in the southern hemisphere is lacking. The aim of this study was to examine the epidemiology of critically ill, obstetric patients in Australia and New Zealand.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.318
Teacher spread0.265 · 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 teacher head, 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueObstetric Anesthesia DigestSame topicMaternal and fetal healthcareFrench-language works237,207