Uniform data collection in routine clinical practice in cardiovascular patients for optimal care, quality control and research: The Utrecht Cardiovascular Cohort
Bibliographic record
Abstract
Background Cardiovascular disease remains the major contributor to morbidity and mortality. In routine care for patients with an elevated cardiovascular risk or with symptomatic cardiovascular disease information is mostly collected in an unstructured manner, making the data of limited use for structural feedback, quality control, learning and scientific research. Objective The Utrecht Cardiovascular Cohort (UCC) initiative aims to create an infrastructure for uniform registration of cardiovascular information in routine clinical practice for patients referred for cardiovascular care at the University Medical Center Utrecht, the Netherlands. This infrastructure will promote optimal care according to guidelines, continuous quality control in a learning healthcare system and creation of a research database. Methods The UCC comprises three parts. UCC-1 comprises enrolment of all eligible cardiovascular patients in whom the same information will be collected, based on the Dutch cardiovascular management guideline. A sample of UCC-1 will be invited for UCC-2. UCC-2 involves an enrichment through extensive clinical measurements with emphasis on heart failure, cerebral ischaemia, arterial aneurysms, diabetes mellitus and elevated blood pressure. UCC-3 comprises on-top studies, with in-depth measurements in smaller groups of participants typically based on dedicated project grants. All participants are followed up for morbidity and mortality through linkage with national registries. Conclusion In a multidisciplinary effort with physicians, patients and researchers the UCC sets a benchmark for a learning cardiovascular healthcare system. UCC offers an invaluable resource for future high quality care as well as for first-class research for investigators.
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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 | 0.075 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".