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Record W3041260773 · doi:10.1007/s11739-020-02425-w

Impact of COVID-19 on liver function: results from an internal medicine unit in Northern Italy

2020· article· en· W3041260773 on OpenAlexaff
Marco Vincenzo Lenti, Federica Borrelli de Andreis, Ivan Pellegrino, Catherine Klersy, Stefania Merli, Emanuela Miceli, Nicola Aronico, Caterina Mengoli, M. Di Stefano, Sara Cococcia, Giovanni Santacroce, Simone Soriano, Federica Melazzini, Mariangela Delliponti, Fausto Baldanti, Antonio Triarico, Gino Roberto Corazza, Massimo Pinzani, Antonio Di Sabatino, Gaetano Bergamaschi, Giampiera Bertolino, Silvia Codega, Filippo Costanzo, Roberto Cresci, Giuseppe Derosa, Francesco Falaschi, Carmine Iadarola, Elisabetta Lovati, Pietro Carlo Lucotti, Alessandra Martignoni, Amedeo Mugellini, Chiara Muggia, Patrizia Noris, Elisabetta Pagani, Ilaria Palumbo, Alessandro Pecci, Tiziano Perrone, Carla Pieresca, Paola Preti, Mariaconcetta Russo, C. Sgarlata, Luisa Siciliani, Andrea Staniscia, Francesca Torello Vjera, Giovanna Achilli, Andrea Agostinelli, Valentina Antoci, Alessia Ballesio, F Banfi, Chiara Barteselli, Irene Benedetti, M Brattoli, Francesca Calabretta, Ginevra Cambiè, Roberta Canta, Federico Conca, Luigi Coppola, Elisa Maria Cremonte, Gabriele Croce, Virginia Del Rio, Francesco Di Terlizzi, Maria Giovanna Ferrari, Sara Ferrari, Anna Fiengo, Tommaso Forni, Giulia Freddi, Chiara Frigerio, F Fumoso, Alessandra Fusco, Margherita Gabba, Matteo Garolfi, Antonella Gentile, Giulia Gori, G.F. De Grandi, Paolo Grimaldi, Alice Lampugnani, F Lapia, Federica Lepore, Gianluca Lettieri, Jacopo Mambella, C. Mercanti, Francesco Mordà, Alba Nardone, Luca Pace, Lucia Padovini, Alessandro Parodi, Lavinia Pitotti, Margherita Reduzzi, Giovanni Rigano, Giorgio Rotola, Umberto Sabatini, Lucia Salvi, Jessica Savioli, Carmine Spataro, Debora Stefani

Bibliographic record

VenueInternal and Emergency Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineInternal medicineLiver diseaseIntensive care unitDecompensationLiver function testsBilirubinLiver functionGastroenterologyCoronavirus disease 2019 (COVID-19)Chronic liver diseaseAlanine aminotransferaseCirrhosisDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Little is known regarding coronavirus disease 2019 (COVID-19) clinical spectrum in non-Asian populations. We herein describe the impact of COVID-19 on liver function in 100 COVID-19 consecutive patients (median age 70 years, range 25–97; 79 males) who were admitted to our internal medicine unit in March 2020. We retrospectively assessed liver function tests, taking into account demographic characteristics and clinical outcome. A patient was considered as having liver injury when alanine aminotransferase (ALT) was > 50 mU/ml, gamma-glutamyl transpeptidase (GGT) > 50 mU/ml, or total bilirubin > 1.1 mg/dl. Spearman correlation coefficient for laboratory data and bivariable analysis for mortality and/or need for intensive care were assessed. A minority of patients (18.6%) were obese, and most patients were non- or moderate-drinkers (88.5%). Liver function tests were altered in 62.4% of patients, and improved during follow-up. None of the seven patients with known chronic liver disease had liver decompensation. Only one patient developed acute liver failure. In patients with altered liver function tests, PaO 2 /FiO 2 < 200 was associated with greater mortality and need for intensive care (HR 2.34, 95% CI 1.07–5.11, p = 0.033). To conclude, a high prevalence of altered liver function tests was noticed in Italian patients with COVID-19, and this was associated with worse outcomes when developing severe acute respiratory distress syndrome.

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)0.001
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.031
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)0.0050.000

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 head0.161
GPT teacher head0.474
Teacher spread0.314 · 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 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

Citations50
Published2020
Admission routes1
Has abstractyes

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