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Record W3190998551 · doi:10.1097/jcn.0000000000000848

Reported Signs, Symptoms, and Diagnostic Tests Before Cardiotoxicity Among Women With Breast Cancer: A Pilot Study

2021· article· en· W3190998551 on OpenAlexaff
Mina Attin, Karen Reifenstein, Sakshi Mehta, Kimberly Arcoleo, Caiji Lin, Eugene Storozynsky

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsKimberly-Clark (Canada)
FundersNational Institute on Minority Health and Health Disparities
KeywordsCardiotoxicityBreast cancerMedicineInternal medicineCancerVital signsSpearman's rank correlation coefficientCorrelationOncologySurgeryChemotherapyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiotoxicity after cancer treatment is a potentially preventable life-threatening complication among women with breast cancer. There is no algorithm to identify women with breast cancer at risk of cardiotoxicity. OBJECTIVES: We quantified signs and symptoms as well as selected laboratory values among women with breast cancer who developed cardiotoxicity. METHODS: The clinical characteristics (n = 15) were collected from electronic health records. Spearman correlation coefficients and a nonparametric statistical test were used to analyze data. RESULTS: Significant statistical differences were detected in the laboratory values comparing the first and second half of 6 months before cardiotoxicity including alanine aminotransferase (U/L) (30.67 ± 26.27 and 42.31 ± 35.65, respectively; P = .03, Cohen's d = 0.37). A negative correlation was found between estimated glomerular filtration rate and new onset of more than 1 sign or symptom (Spearman's ρ = -0.5, P = .06). CONCLUSIONS: Investigating clinical characteristics before cardiotoxicity may determine the mechanism(s) and identify high-risk patients.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.558
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

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