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Record W2914251017 · doi:10.1155/2019/9546931

Sex-Specific Cut-Offs for High-Sensitivity Cardiac Troponin: Is Less More?

2019· review· en· W2914251017 on OpenAlexaff
Giulio Francesco Romiti, Roberto Cangemi, Eleonora Ruscio, Susanna Sciomer, Federica Moscucci, Marianna Vincenti, Clara Crescioli, Marco Proietti, Stefania Basili, Valeria Raparelli

Bibliographic record

VenueCardiovascular Therapeutics · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsMcGill University Health Centre
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsMedicineChest painMyocardial infarctionAcute coronary syndromeClinical PracticeTroponinIntensive care medicineCardiologyPhysical therapy

Abstract

fetched live from OpenAlex

Management of patients presenting to the Emergency Department with chest pain is continuously evolving. In the setting of acute coronary syndrome, the availability of high-sensitivity cardiac troponin assays (hs-cTn) has allowed for the development of algorithms aimed at rapidly assessing the risk of an ongoing myocardial infarction. However, concerns were raised about the massive application of such a simplified approach to heterogeneous real-world populations. As a result, there is a potential risk of underdiagnosis in several clusters of patients, including women, for whom a lower threshold for hs-cTn was suggested to be more appropriate. Implementation in clinical practice of sex-tailored cut-off values for hs-cTn represents a hot topic due to the need to reduce inequality and improve diagnostic performance in females. The aim of this review is to summarize current evidence on sex-specific cut-off values of hs-cTn and their application and usefulness in clinical practice. We also offer an extensive overview of thresholds reported in literature and of the mechanisms underlying such differences among sexes, suggesting possible explanations about debated issues.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.014
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.138
GPT teacher head0.368
Teacher spread0.230 · 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 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

Citations50
Published2019
Admission routes1
Has abstractyes

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