MétaCan
Menu
Back to cohort
Record W2048117255 · doi:10.1186/1532-429x-16-s1-p104

Differentiation of physiologic versus pathologic basal septal fibrosis: Proposed diagnostic criteria and associations with clinical and CMR-based markers of cardiovascular disease

2014· article· en· W2048117255 on OpenAlexaff
Sebastien X Joncas, Louis Kolman, Carmen Lydell, Sarah Weeks, Andrew G. Howarth, Naeem Merchant, James A. White

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of CalgaryUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineAngiologyCardiologyBasal (medicine)Internal medicineDiseaseClinical significanceHypertrophic cardiomyopathyPathologyFibrosisSarcoidosisCardiomyopathyHeart failure

Abstract

fetched live from OpenAlex

Abnormal late gadolinium enhancement (LGE) is commonly identified in the basal septum of patients with dilated cardiomyopathy, hypertrophic cardiomyopathy and sarcoidosis, and has been associated with major adverse events. However, basal septal LGE may also be seen in otherwise normal individuals (Figure 1 ) and may be "physiologic". No diagnostic criteria to differentiate the latter have been established, and both its prevalence and clinical significance remain uncertain. In this study we propose such criteria and examine its prevalence and association with markers of cardiovascular disease. (A) Apical long axis 3-chamber late gadolinium enhancement (LGE) image in a patient with "physiologic basal septal LGE" according to the criteria of; (1) direct contact with the aortic root, and (2) decreasing signal intensity apically . (B) Basal short axis view with mid myocardial late gadolinium enhancement. A total of 615 consecutive LGE CMR studies were evaluated. Patients with prior valvular surgery (n = 91) or congenital heart disease (n = 94) were excluded, resulting in 430 studies. All were blindly scored for the presence of "physiologic septal LGE" according to pre-defined criteria, as follows; (1) LGE in direct contact with the aortic root, AND (2) decreasing in signal intensity towards the apex (see Figure 1 ). Baseline clinical and CMR-based measures of structural heart disease, inclusive of total LGE volume (> 5SD threshold) were compared between those with and without diagnostic criteria being met. Mean age and LVEF of the entire population were 55.8 ± 14.5 years and 49.1 ± 21.7%, respectively. A total of 73 patients (20.4%) met criteria for "physiologic basal septal LGE". As shown in Table 1 no association of this finding with any other clinical or CMR markers of cardiovascular disease was identified. The only identified difference identified among those with the finding was a slightly higher LV ejection fraction (53.8 ± 19.4 vs. 48.1 ± 22.2, p = 0.045) and age at time of imaging (59.2 ± 12.7 vs. 55.1 ± 14.9, p = 0.030). No association was seen between the presence of "physiologic basal septal LGE" and total LGE (inclusive of all patterns of disease), measuring 10.9 ± 14.3% by signal-threshold based quantification. By comparison, those patients having basal septal LGE but not meeting "physiologic" criteria showed a significantly lower mean LVEF of (30.2 ± 14.6% (p < 0.001) and higher total burden of LGE (13.4 ± 14.9%, p = 0.025). A similar prevalence of "physiologic basal septal LGE" was found in a population of 35 healthy volunteers. Among a large CMR referral population "physiologic basal septal LGE" was identified in 20% of all patients. This finding does not show association with any clinical or CMR-based marker of cardiovascular disease. While outcome-based studies are both preferred and required, the proposed criteria for "physiologic basal septal LGE" provides a practical tool for the differentiation of benign versus pathologic septal LGE in patients referred for CMR.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Magnetic ResonanceSame topicCardiomyopathy and Myosin StudiesFrench-language works237,207