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Do Quantitative Blood Loss Measurements and Postpartum Hemorrhage Protocols Actually Make a Difference? Yes, No, and Maybe

2021· article· en· W4255556459 on OpenAlexaff
Anthony Chau, M.K. Farber

Bibliographic record

VenueObstetric Anesthesia Digest · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsB.C. Women's Hospital & Health Centre
Fundersnot available
KeywordsMedicineMaternal morbidityProtocol (science)Blood lossObstetricsMaternal deathGeneral partnershipIntensive care medicinePregnancyEmergency medicineSurgeryEnvironmental healthPopulationPathologyAlternative medicine

Abstract

fetched live from OpenAlex

( Int J Obstet Anesth . 2020;42:1–3) Postpartum hemorrhage (PPH) is a leading cause of severe maternal morbidity. In California, hospitals that adopted the United States National Partnership for Maternal Safety (NPMS) Consensus Bundle for Obstetric Hemorrhage reduced hemorrhage-related severe maternal morbidity by 20.8%. Quantitation of blood loss (QBL) and protocol utilization during PPH were 2 elements of the NPMS bundle and have been evaluated in recent studies, which may shed light on the contribution of these 2 practices to the observed improved outcomes.

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)0.000
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 score0.042
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.052
GPT teacher head0.308
Teacher spread0.256 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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