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Record W2253190791 · doi:10.1097/gco.0000000000000218

Amniotic fluid embolism

2015· review· en· W2253190791 on OpenAlexfundno aff
Kathryn J. Balinger, Melissa T. Chu Lam, Heidi H. Hon, Stanislaw P. Stawicki, James N. Anasti

Bibliographic record

VenueCurrent Opinion in Obstetrics & Gynecology · 2015
Typereview
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsnot available
FundersUniversity Health Network
KeywordsMedicineAmniotic fluid embolismIntensive care medicineCoagulopathySeptic shockDisseminated intravascular coagulationDiseaseResuscitationIncidence (geometry)PregnancyPediatricsInternal medicineSurgerySepsis

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article reviews the incidence, pathophysiology, risk factors, diagnosis, and management of amniotic fluid embolism (AFE). RECENT FINDINGS: AFE is a leading cause of maternal morbidity and mortality despite an incidence of approximately 7 to 8 per 100,000 births. Recent reevaluation of AFE suggests that the presence of fetal tissue in maternal circulation alone is not sufficient to cause the clinical syndrome, but rather an individual's response to this fetal tissue. The 'anaphylactoid reaction' associated with AFE shares many clinical and metabolic aspects of septic shock. Acute dyspnea followed by cardiovascular collapse, coagulopathy, and neurological symptoms, such as coma and seizures may all be associated with the clinical AFE syndrome. Specific biochemical markers have been described, but are of limited clinical value because of the rapid progression of the disease process. Treatment is based on an interdisciplinary approach that consists of a combination of prompt, aggressive hemodynamic resuscitation, provision of end-organ support, correction of hemostatic disorders, and delivery. SUMMARY: Although AFE cannot be prevented, early diagnosis and intervention may lead to better outcomes for both the mother and the fetus. Clinical suspicion, traditional laboratory data, or intravascular cellular debris (demonstrated only in 50% of patients) are insufficient to make a definitive diagnosis of AFE. An evolving array of novel biomarkers may help differentiate AFE from other conditions, but none of them currently provide sufficient 'early warning' ability to make real-time impact on diagnosis and/or treatment of AFE.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.220
GPT teacher head0.463
Teacher spread0.243 · 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.

Study designNot applicable
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

Citations15
Published2015
Admission routes1
Has abstractyes

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