Cohort Profile: Swiss BioRef: The building blocks of a nationwide IT infrastructure in Switzerland for generating precise reference intervals
Bibliographic record
Abstract
Abstract Purpose Swiss BioRef is a nation-wide multicentre infrastructure project, the aim of which is to become a sustainable framework for the estimation and assessment of patient-group-specific reference intervals in laboratory medicine and beyond. In this unprecedented effort, nation-wide multidimensional data from multiple clinical laboratory databases has been combined under the common interoperable semantic framework of the Swiss Personalized Health Network (SPHN) initiative. The consolidated effort enables creating extremely detailed patient group-specific queries via intuitive web applications, allowing the generation of individualised, covariate adjusted reference intervals on-the-fly. Participants The project is a collaborative effort of four major hospitals in Switzerland, the University Hospital Bern (Inselspital, “Insel”), University Hospital Lausanne (CHUV), Swiss Spinal Cord Injury Cohort (“SwiSCI”) and the University Children’s Hospital Zurich (“KiSpi”), and two academic groups in Bern and in Lausanne. Findings to date Within the infrastructure we deployed, the laboratory data from four major hospitals (approximately 9 million measurements from 250’000 patients) is made available to two conceptually different web applications (one centralised and statistically detailed, one decentralised using distributed computing). They enable the inference of reference intervals for more than 40 blood test variables from clinical chemistry, haematology, point-of-care-testing, and coagulation testing, with various patient factors (such as age, sex and a combination of ICD-10 defined diagnoses) and analytical factors (such as type or unique identifiers) that can be used to generate precise reference intervals for the respective groups. Future plan Now that all required basic infrastructure elements for Swiss BioRef are deployed, we are evaluating inter-cohort transferability of semantic standards, “change tracking” in merged databases and biological variation of the blood test variables, in order to generate precise reference intervals. While adjusting the developed web-interfaces to suit the needs of the various end-users, we additionally plan to onboard new national and international partners. Strengths and limitations of this study The Swiss BioRef project is the first multi-cohort infrastructure in Switzerland for the estimation of precise reference intervals in laboratory medicine. With the BioRef consortium agreement a common framework for multi-cohort data sharing, hosting, and accessing has been thoroughly defined. The definition of interoperable data formats and data encoding for Swiss BioRef permits the fusion of the various data sources into a unified infrastructure. Due to differing data management systems at the individual clinical data warehouses, the harmonisation of data contributions requires significant effort which limits direct data provision. Two different web applications with varying data access architectures enable researchers to map the individual complexity of their patients into a substantiated statistical analysis to infer precise and highly relevant reference intervals. Needless to say, anticipating the requirements of an increasingly diverse user base remains a challenging task. Due to the modular expandable architecture of Swiss BioRef, potential national and international partners can easily access and even join the network.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".