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Record W2750914534 · doi:10.5603/gp.a2017.0083

Venous thromboembolism in obese pregnant women: approach to diagnosis and management

2017· review· en· W2750914534 on OpenAlexaff
Ann Kinga Malinowski, Dorota Bomba‐Opoń, Jacqueline Parrish, Urszula Sarzyńska, Dan Farine

Bibliographic record

VenueGinekologia Polska · 2017
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicinePulmonary embolismDeep veinPregnancyVenous thromboembolismObesityThrombosisVenous thrombosisPopulationRisk factorIntensive care medicineObstetricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Venous thromboembolism (VTE) remains among the leading causes of maternal mortality in the developed world, presenting variably as deep vein thrombosis (DVT), pulmonary embolism (PE) or cerebral vein thrombosis (CVT), among others. Obesity in particular has been recognized as the principal contributing factor to the risk of VTE in pregnancy and with the global increase in the rates of obesity affecting reproductive age women, heightened awareness of the risk and consequences of VTE in this population are vital. Thus, prophylaxis, diagnosis and treatment of VTE in the obese gravida are discussed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.113
GPT teacher head0.375
Teacher spread0.262 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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