MétaCan
Menu
Back to cohort

The Dangers of Cardiac Myxomas

2007· review· en· W2024798291 on OpenAlexaff
Sylvia Roschkov, Darlene Rebeyka, Jean K. Mah, Gayle Urquhart

Bibliographic record

VenueProgress in Cardiovascular Nursing · 2007
Typereview
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsStollery Children's HospitalUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineMyxomaPresentation (obstetrics)Cardiac TumorsIntensive care medicineDiseaseQuality of life (healthcare)Case presentationSurgeryCardiologyInternal medicineNursing

Abstract

fetched live from OpenAlex

A variety of cardiac tumors have been acknowledged in the literature since the 16th century as rare forms of cardiac disease. Of the primary tumors, myxomas account for at least 30% to 50% of benign tumors. Despite significant advances in cardiac diagnostics leading to early recognition of myxomas, the potential for deleterious effects secondary to embolic complications remains high. The purpose of this paper is to provide nurses with an understanding of the epidemiology, pathology, clinical presentation, and assessment of individuals with cardiac myxomas. A case presentation is used to illustrate how the misdiagnosis of cardiac myxoma led to a delay in patient treatment. Prompt recognition, diagnosis, and treatment are important in improving patient outcomes and quality of life. Due to the infrequency of cardiac myxomas, ensuring appropriate preoperative and postoperative nursing care to the patient with a cardiac myxoma is essential.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.378
Teacher spread0.330 · 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
GenreReview

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

Citations21
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProgress in Cardiovascular NursingSame topicCardiac tumors and thrombiFrench-language works237,207