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Record W4385263891 · doi:10.5588/ijtld.23.0085

Clinical standards for drug-susceptible TB in children and adolescents

2023· article· en· W4385263891 on OpenAlexafffund
S S Chiang, Stephen M. Graham, H. Simon Schaaf, Ben J. Marais, Clemax Couto Sant’Anna, Sangeeta Sharma, Jeffrey R. Starke, Rina Triasih, Jay Achar, Farhana Amanullah, Lisa Armitage, Rafaela Baroni Aurílio, W Chris Buck, Rosella Centis, Chishala Chabala, Andrea T. Cruz, Anne‐Marie Demers, Karen Du Preez, Anthony Enimil, Jennifer Furin, Anthony J. Garcia‐Prats, N. E. Gonzalez, Graeme Hoddinott, Petros Isaakidis, Devan Jaganath, S. K. Kabra, Beate Kampmann, Alexander Kay, Ian Kitai, Elisa López‐Varela, Elizabeth Maleche‐Obimbo, Francesco Malaspina, Jürg Niederbacher Velásquez, James Nuttall, Jacquie Oliwa, Isadora Andrade, Carlos M. Pérez‐Vélez, Helena Rabie, James A. Seddon, Moorine Sekadde, Adong Shen, Alena Skrahina, Antoni Soriano‐Arandes, Andrew P. Steenhoff, Marc Tebruegge, Marco Tovar, Bazarragchaa Tsogt, Marieke M. van der Zalm, Henry Welch, Giovanni Battista Migliori

Bibliographic record

VenueThe International Journal of Tuberculosis and Lung Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationCentre Hospitalier Universitaire Sainte-Justine
FundersEuropean and Developing Countries Clinical Trials PartnershipNational Institute of Allergy and Infectious DiseasesKwame Nkrumah University of Science and TechnologyFaculty of Medicine and Health, University of SydneyFogarty International CenterUniversidade do Estado do Rio de JaneiroUniversity of IoanninaUniversity of California, Los AngelesCase Western Reserve UniversityDavid Geffen School of Medicine, University of California, Los AngelesUniversidad Industrial de SantanderImperial College LondonUniversidade Federal do Rio de JaneiroUniversity of Cape TownUniversity of TorontoNational Heart, Lung, and Blood InstituteBeijing Children's Hospital, Capital Medical UniversityHospital for Sick ChildrenUniversitat de BarcelonaCapital Medical UniversityWellcome TrustUniversidad de Buenos AiresUniversiteit StellenboschUniversity of Wisconsin-Madison
KeywordsMedicinePediatricsGold standard (test)RegimenDelphi methodDiseaseIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: These clinical standards aim to provide guidance for diagnosis, treatment, and management of drug-susceptible TB in children and adolescents.METHODS: Fifty-two global experts in paediatric TB participated in a Delphi consensus process. After eight rounds of revisions, 51/52 (98%) participants endorsed the final document.RESULTS: Eight standards were identified: Standard 1, Age and developmental stage are critical considerations in the assessment and management of TB; Standard 2, Children and adolescents with symptoms and signs of TB disease should undergo prompt evaluation, and diagnosis and treatment initiation should not depend on microbiological confirmation; Standard 3, Treatment initiation is particularly urgent in children and adolescents with presumptive TB meningitis and disseminated (miliary) TB; Standard 4, Children and adolescents should be treated with an appropriate weight-based regimen; Standard 5, Treating TB infection (TBI) is important to prevent disease; Standard 6, Children and adolescents should receive home-based/community-based treatment support whenever possible; Standard 7, Children, adolescents, and their families should be provided age-appropriate support to optimise engagement in care and clinical outcomes; and Standard 8, Case reporting and contact tracing should be conducted for each child and adolescent.CONCLUSION: These consensus-based clinical standards, which should be adapted to local contexts, will improve the care of children and adolescents affected by TB.

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)0.138
metaresearch head score (Gemma)0.220
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.138
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1380.220
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0030.005
Scholarly communication0.0040.003
Open science0.0060.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.019
GPT teacher head0.394
Teacher spread0.375 · 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
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

Citations20
Published2023
Admission routes2
Has abstractyes

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