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Record W2790233690 · doi:10.1097/mop.0000000000000626

Approach to pulmonary vascular disease in the ICU

2018· review· en· W2790233690 on OpenAlexaff
Georgia Brown, Tilman Humpl

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2018
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsHospital for Sick Children
FundersXenios
KeywordsMedicineIntensive care medicinePulmonary hypertensionAfterloadVascular resistanceCardiac catheterizationDiseaseCardiologyVentricleInternal medicineHemodynamics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Pulmonary vascular disease (PVD) complicates the course of many cardiovascular, pulmonary and other systemic diseases in children. The physiological sequelae (pulmonary hypertension and elevated pulmonary vascular resistance) can overwhelm the right ventricle and lead to circulatory collapse. Despite the common end-point, the preceding pathophysiology is complex and variable and requires a tailored approach to diagnosis and management. In this article, we will review the most recent evidence and explore an approach to current controversies in the diagnosis and management of common or challenging patient subgroups. RECENT FINDINGS: New methods of interpreting data derived from echocardiography and cardiac magnetic resonance imaging may assist in risk stratification and response to therapy. In specific patient subgroups, standard pharmacological therapies to reduce right ventricle afterload may be overutilized, ineffective and in some cases harmful. In the patient failing pharmacological therapy, new and novel techniques are being explored including temporary extracorporeal mechanical circulatory support, pumpless lung assist devices and novel surgical and catheterization procedures. SUMMARY: PVD is a diverse entity, and attention to the underlying pathophysiology is essential for appropriate management. Despite significant advances in our understanding, the majority of data comes from small uncontrolled studies and must be interpreted with caution.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.158
GPT teacher head0.414
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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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