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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 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)0.004
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 score0.199
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.053
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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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