Characteristics of intensive care unit registries - findings from the Global Registry ICU Datasets (GRID) survey
Bibliographic record
Abstract
BACKGROUND: Intensive care unit registries, which aim to improve the quality of intensive care unit care through benchmarking and quality improvement initiatives, are active worldwide, with considerable dishomogeneity. We aimed to map core datasets, additional variables, and research activities of these registries. METHODS: A cross-sectional survey was disseminated to registry leads between October 2023 and June 2024. The survey was structured into four main topics: registry characteristics and coverage, core dataset features, additional modules, and registry-enabled research. RESULTS: Leads of 34/42 national registries responded (response rate 81%), covering 3,337 intensive care units, with a larger representation from South America. Systematized nomenclature of medicine, clinical terms, and customized categorical classifications were the main nomenclatures used. All registries except one employed a severity of illness score/risk prediction model. The SOFA score was reported by 88% of registries. Organ support measures were often recorded, including mechanical ventilation (97%), vasopressor administration (86%) and renal replacement therapy (86%). Three out of four intensive care unit registries coded interventions such as intubations, intravenous lines and tracheostomies. Additional datasets differed, with many use cases for nosocomial infection burden, bed availability and staffing resources. Over half of intensive care unit registries had current structured quality improvement initiatives. Registry-enabled observational research was reported in 46% of registries, while interventional studies were reported in only 22%. CONCLUSION: Over three thousand intensive care units in 35 countries participate in an intensive care unit registry. Despite heterogeneity in coding systems, risk models, and additional datasets, we identify several areas of convergence that may inform a future shared core dataset. There is potential for further intensive care unit registry-based research, particularly interventional.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".