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Record W4407150164 · doi:10.1136/bmjopen-2024-097455

Azithromycin to prevent acute lower respiratory infections among Australian and New Zealand First Nations and Timorese children (PETAL trial): study protocol for a multicentre, international, double-blind, randomised controlled trial

2025· article· en· W4407150164 on OpenAlexaboutno aff
Gabrielle B. McCallum, Catherine A. Byrnes, Peter S Morris, Keith Grimwood, Robyn L. Marsh, Mark D. Chatfield, Emily R Bowden, Kobi L. Schutz, Nevio Sarmento, Nicholas Fancourt, Joshua Francis, Yuejen Zhao, Adriano Vieira, Kim M. Hare, Dennis Bonney, Adrian Trenholme, Shirley Lawrence, Felicity Marwick, Bronwyn Karvonen, Carolyn Maclennan, Christine Connors, Heidi Smith‐Vaughan, M Lay, Endang Soares da Silva, Anne B. Chang

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsMedicineAzithromycinRandomized controlled trialBronchiectasisPediatricsPlaceboPlacebo-controlled studyPsychological interventionInternal medicineAntibioticsLungDouble blindAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Acute lower respiratory infections (ALRIs) remain the leading causes of repeated hospitalisations among young disadvantaged Australian and New Zealand First Nations and Timorese children. Severe (hospitalised) and recurrent ALRIs in the first years of life are associated with future chronic lung diseases (eg, bronchiectasis) and impaired lung function. Despite the high burden and long-term consequences of severe ALRIs, clinical, evidence-based and feasible interventions (other than vaccine programmes) that reduce ALRI hospitalisations in children are limited. This randomised controlled trial (RCT) will address this unmet need by trialling a commonly prescribed macrolide antibiotic (azithromycin) for 6-12 months. Long-term azithromycin was chosen as it reduces ALRI rates by 50% in Australian and New Zealand First Nations children with chronic suppurative lung disease or bronchiectasis. The aim of this multicentre, international, double-blind, placebo-containing RCT is to determine whether 6-12 months of weekly azithromycin administered to Australian and New Zealand First Nations and Timorese children after their hospitalisation with an ALRI reduces subsequent ALRIs compared with placebo. Our primary hypothesis is that children receiving long-term azithromycin will have fewer medically attended ALRIs over the intervention period than those receiving placebo. METHODS AND ANALYSIS: We will recruit 160 Australian and New Zealand First Nations and Timorese children aged <2 years to a parallel, superiority RCT across four hospitals from three countries (Australia, New Zealand and Timor-Leste). The primary outcome is the rate of medically attended ALRIs during the intervention period. The secondary outcomes are the rates and proportions of children with ALRI-related hospitalisation, chronic symptoms/signs suggestive of underlying chronic suppurative lung disease or bronchiectasis, serious adverse events, and antimicrobial resistance in the upper airways, and cost-effectiveness analyses. ETHICS AND DISSEMINATION: The Human Research Ethics Committees of the Northern Territory Department of Health and Menzies School of Health Research (Australia), Health and Disability Ethics Committee (New Zealand) and the Institute National of Health-Research Technical Committee (Timor-Leste) approved this study. The study outcomes will be disseminated to academic and medical communities via international peer-reviewed journals and conference presentations, and findings reported to health departments and consumer-based health organisations. CLINICAL TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry ACTRN12619000456156.

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)0.017
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0030.001
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0530.008

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 head0.083
GPT teacher head0.459
Teacher spread0.376 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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