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Record W2461527560 · doi:10.14740/jocmr2617w

Estimating Right Atrial Pressure Using Ultrasounds: An Old Issue Revisited With New Methods

2016· review· en· W2461527560 on OpenAlexvenueno aff
Renato De Vecchis, Cesare Baldi, Giuseppe Giandomenico, Marco Di Maio, Anna Giasi, Carmela Cioppa

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typereview
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDecompensationCardiac decompensationLeft atrial pressureCardiologyRepresentation (politics)Internal medicineHemodynamicsIntensive care medicineHeart failureLaw

Abstract

fetched live from OpenAlex

Knowledge of the right atrial pressure (RAP) values is critical to ascertain the existence of a state of hemodynamic congestion, irrespective of the possible presence of signs and symptoms of clinical congestion and cardiac overload that can be lacking in some conditions of concealed or clinically misleading cardiac decompensation. In addition, a more reliable estimate of RAP would make it possible to determine more accurately also the systolic pulmonary arterial pressure with the only echocardiographic methods. The authors briefly illustrate some of the criteria that have been implemented to obtain a non-invasive RAP estimate, some of which have been approved by current guidelines and others are still awaiting official endorsement from the Scientific Societies of Cardiology. There is a representation of the sometimes opposing views of researchers who have studied the problem, and the prospects for development of new diagnostic criteria are outlined, in particular those derived from the matched use of two- and three-dimensional echocardiographic parameters.

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)0.044
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.445
GPT teacher head0.677
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations28
Published2016
Admission routes1
Has abstractyes

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