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Record W1553912500 · doi:10.1002/9781118742440.ch45

The Echocardiographic Assessment of Pulmonary Arterial Hypertension

2016· other· en· W1553912500 on OpenAlexaff
Lindsay M. Ryerson, Jeffrey F. Smallhorn

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsPulmonary hypertensionCardiologyMedicinePulmonary arteryRegurgitation (circulation)Internal medicineVascular resistanceBlood pressureRadiology

Abstract

fetched live from OpenAlex

Echocardiography has become an important screening tool for the assessment of the patient with pulmonary arterial hypertension. Although it provides an indirect assessment and cannot be used to determine pulmonary vascular resistance the technique is at the forefront of investigations, and in many instances is the only one necessary to exclude the diagnosis. There are multiple techniques at the disposal of the echocardiographer which demonstrates the versatility of this technique. The technique relies heavily on the presence of tricuspid and/or pulmonary regurgitation to provide an absolute value, and fortunately these are present in many individuals undergoing echocardiographic assessment. In those where right-sided regurgitation is absent, indirect assessment can be of value in determining the presence or absence of raised pulmonary artery pressure. As well, echocardiography can be used to assess the impact of pulmonary hypertension on ventricular function, with some of the newer tools providing valuable insight into ventricular mechanics.

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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.312
Teacher spread0.292 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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