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Record W4396968099 · doi:10.1093/eurheartjsupp/suae034

Italian Association of Hospital Cardiologists Position Paper ‘Gender discrepancy: time to implement gender-based clinical management’

2024· article· en· W4396968099 on OpenAlexaff
Fabiana Lucà, Daniela Pavan, Michele Massimo Gulizia, Maria Teresa Manes, Maurizio Giuseppe Abrignani, Francesco Benedetto, Irma Bisceglia, Silvana Brigido, Pasquale Caldarola, Raimondo Calvanese, Maria Laura Canale, Giorgio Caretta, Roberto Ceravolo, Alaide Chieffo, Cristina Chimenti, Stefano Cornara, Ada Cutolo, Stefania Angela Di Fusco, Irene Di Matteo, Concetta Di Nora, Francesco Fattirolli, Silvia Favilli, Giuseppina Maura Francese, Sandro Gelsomino, Giovanna Geraci, Simona Giubilato, Nadia Ingianni, Francesca Lanni, Andrea Montalto, Federico Nardi, Alessandro Navazio, Martina Nesti, Iris Parrini, Annarita Pilleri, Andrea Pozzi, Carmelo Massimiliano Rao, Carmine Riccio, Roberta Rossini, Pietro Scicchitano, Serafina Valente, Giuseppe Zuccalà, Domenico Gabrielli, Massimo Grimaldi, Furio Colivicchi, Fabrizio Oliva

Bibliographic record

VenueEuropean Heart Journal Supplements · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicinePosition paperPosition (finance)Association (psychology)Emergency medicinePathology

Abstract

fetched live from OpenAlex

It has been well assessed that women have been widely under-represented in cardiovascular clinical trials. Moreover, a significant discrepancy in pharmacological and interventional strategies has been reported. Therefore, poor outcomes and more significant mortality have been shown in many diseases. Pharmacokinetic and pharmacodynamic differences in drug metabolism have also been described so that effectiveness could be different according to sex. However, awareness about the gender gap remains too scarce. Consequently, gender-specific guidelines are lacking, and the need for a sex-specific approach has become more evident in the last few years. This paper aims to evaluate different therapeutic approaches to managing the most common women's diseases.

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)0.014
metaresearch head score (Gemma)0.041
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0230.008

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 head0.062
GPT teacher head0.401
Teacher spread0.339 · 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
GenreEditorial

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

Citations9
Published2024
Admission routes1
Has abstractyes

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