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Record W3113304994 · doi:10.1016/j.jaccao.2020.10.011

Strain Imaging in Cardio-Oncology

2020· review· et· W3113304994 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJACC CardioOncology · 2020
Typereview
Languageet
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Cancer InstituteNational Institutes of Health
KeywordsCardiotoxicityMedicineEjection fractionSubclinical infectionStrain (injury)Internal medicineCardiologyRadiologyMedical physicsChemotherapyHeart failure

Abstract

fetched live from OpenAlex

Echocardiographic imaging is crucial for patient management during cardiotoxic cancer therapy. Left ventricular ejection fraction is the most commonly used parameter for identifying left ventricular dysfunction. However, it lacks sensitivity to detect subclinical changes in cardiac function due to cardiotoxic treatment. Global longitudinal strain (GLS) is the best studied strain parameter with established diagnostic and prognostic value. Multiple studies have demonstrated changes in GLS as an early marker of cardiotoxicity. This document serves as a primer to help clinicians in the acquisition and interpretation of strain in cardio-oncology. Cases with embedded videos illustrate a step-by-step approach to obtaining GLS measurements and common pitfalls to avoid. The document includes a concise summary of the indications of GLS in cardio-oncology and its role in guiding oncological therapy. Practical approaches on how to implement strain in the echo laboratory with guidance on training and quality assurance are also discussed.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0100.003
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.046
GPT teacher head0.363
Teacher spread0.317 · 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