Rinaldo Bellomo's seminal contribution to observational research using the ANZICS CORE registry
Bibliographic record
Abstract
Rinaldo Bellomo advanced critical care not only through randomised trials but also through rigorous use of observational data, particularly from the ANZICS Centre for Outcome and Resource Evaluation (ANZICS CORE) Registry. At a time when retrospective analyses were often confined to hypothesis generation, he showed that carefully curated, clinically grounded registry studies could inform policy and change practice. Recognising early the potential of ANZICS CORE to become a leading registry, he worked to strengthen its data architecture and published in journals such as The New England Journal of Medicine and JAMA , helping to spark global dialogue and shape guidelines. Using the Adult Patient Database, he described epidemiological trends, identified clinically relevant questions, designed, justified and evaluated randomised trials, and monitored the uptake of evidence-based practice. His work addressed key challenges in sepsis, acute kidney injury, glycaemic control, temperature management and health equity, and was marked by clear case definitions, extensive sensitivity analyses and transparent reporting. This article reviews selected contributions using ANZICS CORE data and outlines how his legacy endures through the value of these datasets and the many researchers he mentored.
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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 | 0.173 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".