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Record W2116059480 · doi:10.1002/mrm.24531

Quantitative magnetic resonance imaging can distinguish remodeling mechanisms after acute myocardial infarction based on the severity of ischemic insult

2012· article· en· W2116059480 on OpenAlexafffund
Nilesh R. Ghugre, Mihaela Pop, Jennifer Barry, Kim A. Connelly, Graham A. Wright

Bibliographic record

VenueMagnetic Resonance in Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchGE Healthcare
KeywordsMedicineCardiologyInternal medicineMyocardial infarctionEdemaMagnetic resonance imagingInfarctionVasodilationVentricular remodelingOcclusionIschemiaRadiology

Abstract

fetched live from OpenAlex

The type and extent of myocardial infarction encountered clinically is primarily determined by the severity of the initial ischemic insult. The purpose of the study was to differentiate longitudinal fluctuations in remodeling mechanisms in porcine myocardium following different ischemic insult durations. Animals (N = 8) were subjected to coronary balloon occlusion for either 90 or 45 min, followed by reperfusion. Imaging was performed on a 3 T MRI scanner between day-2 and week-6 postinfarction with edema quantified by T2, hemorrhage by T2*, vasodilatory function by blood-oxygenation-level-dependent T2 alterations and infarction/microvascular obstruction by contrast-enhanced imaging. The 90-min model produced large transmural infarcts with hemorrhage and microvascular obstruction, while the 45 min produced small nontransmural and nonhemorrhagic infarction. In the 90-min group, elevation of end-diastolic-volume, reduced cardiac function, persistence of edema, and prolonged vasodilatory dysfunction were all indicative of adverse remodeling; in contrast, the 45-min group showed no signs of adverse remodeling. The 45- and 90-min porcine models seem to be ideal for representing the low- and high-risk patient groups, respectively, commonly encountered in the clinic. Such in vivo characterization will be a key in predicting functional recovery and may potentially allow evaluation of novel therapies targeted to alleviate ischemic injury and prevent microvascular obstruction/hemorrhage.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.276
Teacher spread0.261 · 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.

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

Citations42
Published2012
Admission routes2
Has abstractyes

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