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Record W2320929259 · doi:10.1097/md.0000000000000038

Morphologic Features of the Recipient Heart in Patients Having Cardiac Transplantation and Analysis of the Congruence or Incongruence Between the Clinical and Morphologic Diagnoses

2014· article· en· W2320929259 on OpenAlexaff
William C. Roberts, Carey Camille Roberts, Jong Mi Ko, Giovanni Filardo, John E. Capehart, Shelley Hall

Bibliographic record

VenueMedicine · 2014
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineHeart transplantationMedical diagnosisTransplantationCongruence (geometry)CardiologyIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Cardiac transplantation (CT) has been one of the great medical advances of the last nearly 50 years. We studied the explanted hearts of 314 patients having CT at Baylor University Medical Center Dallas from 1993 to 2012, and compared the morphologic diagnoses to the clinical diagnoses before CT. Among the 314 patients the morphologic and clinical diagnoses were congruent in 272 (87%) and incongruent in 42 (13%). Most of the incongruity occurred among the 166 patients with non-ischemic cardiomyopathy (non-IC) (36/166 [22%]), and of that group the major incongruity occurred among the patients with hypertrophic cardiomyopathy (7/17 [41%]), non-compaction left ventricular cardiomyopathy (NCLVC) (3/3 [100%]), mononuclear myocarditis (3/3 [100%]), arrhythmogenic right ventricular cardiomyopathy (ARVC) (4/4 [100%]), and cardiac sarcoidosis (8/8 [100%]). The phrase "non-IC" is a general term that includes several subsets of cardiac diseases and simply means "insignificant narrowing of 1 or more of the epicardial coronary arteries," but it does not specify the specific cause of the heart failure leading to CT. A number of cardiac illustrations are provided to demonstrate the morphologic variability occurring among the patients with IC and non-IC.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.356
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations47
Published2014
Admission routes1
Has abstractyes

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