P‐IS‐3 | Establishing Reference Intervals for Clinical Laboratory Tests: The Role of Blood Centers
Bibliographic record
Abstract
Clinical laboratory tests require appropriate reference intervals (RI) for accurate interpretation. The importance of RI cannot be understated as many diagnostic and medical treatment decisions are based on laboratory results. Blood centers are ideal locations for reference subject recruitment since blood donors are diverse and vary in age, race, ethnicity, reasonable health status, etc. Interleukin-6 (IL-6) is a pro-inflammatory cytokine that is elevated in various diseases including rheumatoid arthritis, juvenile idiopathic arthritis, and COVID-19. In severe COVID-19 infections, IL-6 levels are 2.9 times higher compared to mild cases, making it a potential therapeutic target and prognostic indicator for moderate-to-severe COVID-19. An independent blood center leveraged access to a large and diverse donor population to evaluate the RI of IL-6 in the community using a new point-of-care (POC) testing device. Whole blood (WB) and serum samples were taken from healthy adults aged >22 years who donated blood at a regional blood center from September to December 2022. IL-6 testing was run on samples using a POC microfluidic immunoanalyzer. Participants were enrolled based on IRB-approved, study-specific inclusion/exclusion criteria. The POC device RI was compared to a peer-reviewed publication on normal values for IL-6 in healthy individuals (https://pubmed.ncbi.nlm.nih.gov/33155686/). A total of 185 donors were screened, of whom 134 were eligible for the study. Most participants were male (58.2%) and Caucasian (76.1%). IL-6 ranges in WB were significantly higher than serum levels, 1–285 pg/mL versus 0–185 pg/mL, (Wilcoxon p < .001, Table 1). The difference between males and females was significant for WB IL-6 (Mann–Whitney p = .001), but not for serum IL-6 (p = .123). The measured IL-6 values positively correlated with age (correlation coefficient WB 0.227 [p = .008]; serum 0.197 [p = .022]). The POC immunoanalyzer has a specificity of 93% (WB) and 94% (serum) when testing for any evidence of disease or inflammation, using a cutoff value based on the first standard deviation.
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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 | 0.041 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".