MétaCan
Menu
Back to cohort
Record W4415252081 · doi:10.5554/22562087.e1169

Dyspnea in pregnancy. Can POCUS be the game changer?

2025· article· en· W4415252081 on OpenAlexaff
Juan Pablo Ghiringhelli, Cristián Arzola, Fabricio B. Zasso

Bibliographic record

VenueColombian Journal of Anesthesiology · 2025
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsAcute pulmonary edemaPulmonary edemaInotropePathologicalAsthmaDifferential diagnosisUltrasoundMedical diagnosis

Abstract

fetched live from OpenAlex

Dyspnea is a common symptom during pregnancy, but it can present a diagnostic challenge. While it may often be attributed to physiological changes it may also signal serious underlying conditions such as asthma crisis, infection, pulmonary edema (PEd), pulmonary embolism, and amniotic fluid embolism. Point-of-care ultrasound (POCUS) is a swift and effective bedside modality for assessing acute or critical medical conditions. It can be used as an alternative or alongside traditional formal ultrasound conducted by a radiology-cardiology service. This review explores the role of POCUS in the parturient experiencing shortness of breath, highlighting the potential value of maternal cardiopulmonary POCUS in obstetric anesthesia, facilitating timely treatment and providing immediate differential diagnosis in potentially unstable patients. As an example, a case is discussed involving a sudden onset of PEd and loss of consciousness in a 48-year-old patient who underwent a category-1 cesarean section under general anesthesia. POCUS identified severely decreased global systolic function and confirmed pulmonary edema through the presence of B-Kerley lines, providing a swift guidance for intraoperative inotropic support and fluid management while excluding other causes of pathological dyspnea in pregnancy.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.066
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.333
Teacher spread0.298 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueColombian Journal of AnesthesiologySame topicUltrasound in Clinical ApplicationsFrench-language works237,207