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Record W4410015017 · doi:10.1016/j.hjc.2025.04.004

NAXCARE: a clinical outcome registry for Naxos disease and related cardiocutaneous syndromes

2025· review· en· W4410015017 on OpenAlexaff
Adalena Tsatsopoulou, Dominic Jr Abrahms, Aris Anastasakis, Loizos Antoniades, Eloisa Arbustini, Euan A. Ashley, Angeliki Asimaki, Cristina Basso, Eduardo Bossone, Julia Cadrin-Turigny, Hugh Calkins, Andreina Carbone, Perry Elliott, Georgios Efthimiadis, Monica Franzese, Alexandra Frogoudaki, Juan R. Gimeno, John McGrath, Jodie Ingles, Juan Pablo Kaski, Andre Keren, George Kohiadakis, Emilia Lazarou, George Lazaros, Stamatios Lerakis, Giuseppe Limongelli, Soultana Meditskou, Luisa Mestroni, Ioanna Metaxa, Emanuele Monda, Alexandros Patrianakos, Kalliopi Pilichou, Alexandros Protonotarios, Ioannis Protonotarios, Salvatore Rega, Angelos G. Rigopoulos, Jeffrey E. Saffitz, Petros Syrris, Matthew R.G. Taylor, Charalambos Vlachopoulos, Zafeirenia Xylouri, William J. McKenna

Bibliographic record

VenueHellenic Journal of Cardiology · 2025
Typereview
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineDisease registryDiseaseDermatologyOutcome (game theory)Intensive care medicinePathology

Abstract

fetched live from OpenAlex

The NAXCARE (NAXos disease and Cardiocutaneous Assessment and Registry for Evaluation) is a global initiative designed to collect, store, and analyze clinical outcomes data on patients with Naxos disease and related cardiocutaneous syndromes (CCS). This registry aims to fill the gaps in clinical knowledge, enhance treatment approaches, and improve patient outcomes by systematically documenting disease progression, genetic profiles, and patient care pathways. The following methodology outlines the registry's design, data collection protocols, management, security measures, and anticipated contributions to research and clinical practice.

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)0.007
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.413
Teacher spread0.354 · 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 designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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