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Record W4225288573 · doi:10.1186/s12916-022-02347-8

Etiological spectrum of persistent fever in the tropics and predictors of ubiquitous infections: a prospective four-country study with pooled analysis

2022· article· en· W4225288573 on OpenAlexaff
Emmanuel Bottieau, Lukas Van Duffel, Sayda El Safi, Kanika Deshpande Koirala, Basudha Khanal, Suman Rijal, Narayan Raj Bhattarai, Thong Phe, Kruy Lim, Déby Mukendi, Jean-Roger Lilo Kalo, Pascal Lutumba, Barbara Barbé, Jan Jacobs, Marjan Van Esbroeck, Nikki Foqué, Achilleas Tsoumanis, Philippe Parola, Cédric P. Yansouni, Marleen Boelaert, Kristien Verdonck, François Chappuis

Bibliographic record

VenueBMC Medicine · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicLeptospirosis research and findings
Canadian institutionsMcGill University Health Centre
FundersSeventh Framework ProgrammeEuropean Commission
KeywordsMedicineTyphoid feverMalariaLeptospirosisBrucellosisTuberculosisDengue feverImmunologyInternal medicineRapid diagnostic testLeptospiraTropical medicineVisceral leishmaniasisLeishmaniasisVirologyPathology

Abstract

fetched live from OpenAlex

Abstract Background Persistent fever, defined as fever lasting for 7 days or more at first medical evaluation, has been hardly investigated as a separate clinical entity in the tropics. This study aimed at exploring the frequencies and diagnostic predictors of the ubiquitous priority (i.e., severe and treatable) infections causing persistent fever in the tropics. Methods In six different health settings across four countries in Africa and Asia (Sudan, Democratic Republic of Congo [DRC], Nepal, and Cambodia), consecutive patients aged 5 years or older with persistent fever were prospectively recruited from January 2013 to October 2014. Participants underwent a reference diagnostic workup targeting a pre-established list of 12 epidemiologically relevant priority infections (i.e., malaria, tuberculosis, HIV, enteric fever, leptospirosis, rickettsiosis, brucellosis, melioidosis, relapsing fever, visceral leishmaniasis, human African trypanosomiasis, amebic liver abscess). The likelihood ratios (LRs) of clinical and basic laboratory features were determined by pooling all cases of each identified ubiquitous infection (i.e., found in all countries). In addition, we assessed the diagnostic accuracy of five antibody-based rapid diagnostic tests (RDTs): Typhidot Rapid IgM, Test-it TM Typhoid IgM Lateral Flow Assay, and SD Bioline Salmonella typhi IgG/IgM for Salmonella Typhi infection, and Test-it TM Leptospira IgM Lateral Flow Assay and SD Bioline Leptospira IgG/IgM for leptospirosis. Results A total of 1922 patients (median age: 35 years; female: 51%) were enrolled (Sudan, n = 667; DRC, n = 300; Nepal, n = 577; Cambodia, n = 378). Ubiquitous priority infections were diagnosed in 452 (23.5%) participants and included malaria 8.0% ( n = 154), tuberculosis 6.7% ( n = 129), leptospirosis 4.0% ( n = 77), rickettsiosis 2.3% ( n = 44), enteric fever 1.8% ( n = 34), and new HIV diagnosis 0.7% ( n = 14). The other priority infections were limited to one or two countries. The only features with a positive LR ≥ 3 were diarrhea for enteric fever and elevated alanine aminotransferase level for enteric fever and rickettsiosis. Sensitivities ranged from 29 to 67% for the three RDTs targeting S. Typhi and were 9% and 16% for the two RDTs targeting leptospirosis. Specificities ranged from 86 to 99% for S. Typhi detecting RDTs and were 96% and 97% for leptospirosis RDTs. Conclusions Leptospirosis, rickettsiosis, and enteric fever accounted each for a substantial proportion of the persistent fever caseload across all tropical areas, in addition to malaria, tuberculosis, and HIV. Very few discriminative features were however identified, and RDTs for leptospirosis and Salmonella Typhi infection performed poorly. Improved field diagnostics are urgently needed for these challenging infections. Trial registration NCT01766830 at ClinicalTrials.gov.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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.

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

Citations17
Published2022
Admission routes1
Has abstractyes

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