Reference Interval Harmonization: Harnessing the Power of Big Data Analytics to Derive Common Reference Intervals across Populations and Testing Platforms
Bibliographic record
Abstract
BACKGROUND: Harmonization in laboratory medicine is essential for consistent and accurate clinical decision-making. There is significant and unwarranted variation in reference intervals (RIs) used by laboratories for assays with established analytical traceability. The Canadian Society of Clinical Chemists (CSCC) Working Group on Reference Interval Harmonization (hRI-WG) aims to establish harmonized RIs (hRIs) for laboratory tests and support implementation. METHODS: Harnessing the power of big data, laboratory results were collected across populations and testing platforms to derive common adult RIs for 16 biochemical markers. A novel comprehensive approach was established, including: (a) analysis of big data from community laboratories across Canada; (b) statistical evaluation of age, sex, and analytical differences; (c) derivation of hRIs using the refineR method; and (d) verification of proposed hRIs across 9 laboratories with different instrumentation using serum and plasma samples collected from healthy Canadian adults. RESULTS: Harmonized RIs were calculated for all assays using the refineR method, except free thyroxine. Derived hRIs met proposed verification criterion across 9 laboratories and 5 manufacturers for alkaline phosphatase, albumin (bromocresol green), chloride, lactate dehydrogenase, magnesium, phosphate, potassium (serum), and total protein (serum). Further investigation is needed for some analytes due to failure to meet verification criteria in one or more laboratories (albumin [bromocresol purple], calcium, total carbon dioxide, total bilirubin, and sodium) or concern regarding excessively wide hRIs (alanine aminotransferase, creatinine, and thyroid stimulating hormone). CONCLUSIONS: We report a novel data-driven approach for RI harmonization. Findings support feasibility of RI harmonization for several analytes; however, some presented challenges, highlighting limitations that need to be considered in harmonization and big data analytics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".