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Record W4200246982 · doi:10.1002/9781119536475.ch10

Ventricular Enlargement

2021· other· en· W4200246982 on OpenAlexaff
Antoni Bayés de Luna, Miquel Fiol, Antoni Bayés‐Genís, Adrián Baranchuk, Roberto Elosúa, Manuel Martínez‐Sellés

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsVentricleDilation (metric space)CardiologyInternal medicineMuscle hypertrophyLeft ventricular hypertrophyMedicineAnatomyMathematicsGeometryBlood pressure

Abstract

fetched live from OpenAlex

The electrogenesis of the morphologies that appear with ventricular enlargement are conditioned more by wall hypertrophy than cavity dilation, the opposite of what occurs in atrial enlargement. Echocardiography is a sensitive, innocuous, and reproducible method for determining left ventricular dilation and left ventricular mass. Right ventricular enlargement (RVE) is manifested in patients with systolic overload by wall hypertrophy, which in advanced cases is associated with some degree of dilation. The existence of any type of RVE counteracts more or less the normally dominant left ventricular forces, directing the dominant forces to the right and forward or backward. The increment in left ventricular mass enhances the already dominant vectorial depolarization forces of the left ventricle, which shift backward and often somewhat more upward, in cases of predominance of enlargement of the left ventricular free wall. Regression equations for estimating left ventricular mass were used to identify left ventricular hypertrophy.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.002

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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