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Record W2570292215 · doi:10.1186/s13104-016-2323-9

Multimodality cardiac imaging of a left ventricular papillary fibroelastoma: a case report

2017· article· en· W2570292215 on OpenAlexaff
Rajat Sharma, Mehrdad Golian, Pallav Shah, Davinder S. Jassal, Nasir Shaikh

Bibliographic record

VenueBMC Research Notes · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsPapillary fibroelastomaMultimodalityMedicineCardiologyInternal medicineHeart neoplasmsRadiologyCardiac TumorsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: In the setting of an acute myocardial infarction (AMI), although the most common etiology of a left ventricular (LV) mass identified on multimodality cardiovascular imaging is a thrombus, other possibilities including a vegetation or tumor should be entertained within the differential diagnosis. CASE PRESENTATION: We describe a case of a 43-year-old Caucasian female post AMI diagnosed with a mid-cavitary mass within the LV. Although echocardiography and cardiovascular MRI (CMR) suggested that the mass was a thrombus, given the context of the recent AMI, exploration and surgical excision was completed by the surgeon due to the potential for the mass to embolize. CONCLUSION: The final diagnosis of a papillary fibroelastoma was unique due to its unusual location and large size within the LV cavity. This unique case demonstrates shortcomings of multimodality cardiac imaging in the diagnosis of an atypical mass and the importance of obtaining tissue when clinically safe and feasible.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.095
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.102
GPT teacher head0.428
Teacher spread0.326 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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