Best Poster Presentation in Paediatrics and Child Health—Fellow
Bibliographic record
Abstract
Background: Historically, the majority of clinical trials addressing questions relevant to inpatient general paediatrics have been in tertiary metropolitan, single centres with relatively small patient numbers potentially leading to inequity and poor generalisability. The Children's Inpatient Research Collaboration of Australia and New Zealand (CIRCAN) was created in 2018, with the goal of enhancing opportunities for hospital-based paediatric research studies across Australasia. Aim: The aim of CIRCAN is to foster a robust network of paediatric healthcare professionals and researchers across Australia and New Zealand and lead research studies with the goal to enhance clinical outcomes, improve patient care, improve training opportunities, and accelerate research advancements in paediatric healthcare. Methods: A descriptive analysis of the process and outcomes of the CIRCAN group from 2018 to 2023. Results: Since inception, we have grown membership to 51 paediatric sites across Australia and New Zealand, representing all states and territories. CIRCAN are currently leading a range of studies including: (1) a Delphi study to establish general paediatric research priorities across Australia and New Zealand, (2) a study of the Impact of the COVID-19 Pandemic on general paediatric admissions in Australia, and (3) The ARROW Trial: Assessing the Reduction of Recurrent admissions using OM-85 for the treatment of preschool Wheeze (multi-site randomised controlled trial, funded by NHMRC), with results expected in 2025. In addition, CIRCAN hosts annual research symposiums, and are expanding complimentary local (e.g., PREDICT) and international (e.g., Canada, United States, United Kingdom) collaboratives. Conclusion: The establishment of CIRCAN has substantially increased the opportunities for paediatric healthcare and research in Australia and New Zealand. The collaborative model has not only improved opportunities and clinical outcomes for patients, but also fostered a more integrated and resource-rich environment for clinicians and researchers. These findings affirm the study's hypothesis and highlight the effectiveness of collaborative networks in advancing paediatric healthcare. Our future efforts will focus on expanding CIRCAN's reach and further integrating its research findings into clinical practice, potentially setting a precedent for similar collaborative models in other medical specialities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.464 | 0.181 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".