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Record W4413309719 · doi:10.1007/s11739-025-04084-1

Fever of unknown origin (FUO) FADOI-SIMIT Italian registry: can demographics, comorbidities, and clinical variables predict the etiology of classic FUO?—a prospective Italian study

2025· article· en· W4413309719 on OpenAlexaff
Roberto Luzzati, Verena Zerbato, Luciano Attard, Giulio Virgili, Alessandro Cilli, Ada Zanier, Marco Libanore, Daniela Segala, Andrea Tedesco, Maria Elena Bortolotti, Emanuele Pontali, Marcello Feasi, Azzurra Re, Igor Giarretta, Fulvio Pomero, Elisa Zagarrì, Ercole Concia, Francesco Dentali, Dario Manfellotto, Silvia Cretella, Matteo Bassetti, Daniele Roberto Giacobbe, Laura Labate, Monica Melchio, Bruno Cacopardo, Federica Cosentino, Andrea Marıno, Antonio Cascio, Raffaella Rubino, Antonella Castagna, Anna Danise, Francesco Castelli, Alberto Bergamasco, Giovanni Moioli, Michela Conti, Amina Zaffagnini, Luigi Mario Castello, Pietro Luigi Garavelli, Cecilia Costa, Olivia Bargiacchi, Paolo Grossi, Cristina Rovelli, Luca Dilazzaro, Eleonora Moriconi, Vinicio Manfrin, Marta Mascarello, Eleonora Nicolini, Paola Sterpone, Giovanni Scanelli, Lia Tinillero, Fabrizio Taglietti, Emanuela Caraffa, Moreno Tresoldi, R. Scotti, Irene Vandoni, Fabio Zanini, Francesco Vitale

Bibliographic record

VenueInternal and Emergency Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineFever of unknown originDemographicsEtiologyProspective cohort studyComorbidityMEDLINEPediatricsIntensive care medicineInternal medicineDemography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.366
Teacher spread0.324 · 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 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

Citations1
Published2025
Admission routes1
Has abstractno

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