The impact of pathological fluctuations versus biological variation on the interpretation of laboratory values
Bibliographic record
Abstract
The current criterion used to determine whether the reference interval (RI) can be used for interpretation is based on the index of individuality (II), estimated using biological variation (BV). We hypothesized that pathological variation (PV), the shift between healthy and unhealthy states, varies across biomarkers and may be considered for interpretation with BV. We explored how jointly considering PV and BV impacts the clinical interpretation (diagnostic sensitivity and specificity) of RIs. We propose the index of pathology (IP), a ratio of within- to between-subject coefficients of variation that jointly considers PV and BV. Using a large EHR database from a tertiary care center, we obtained IP estimates for 19 laboratory tests. As a means of comparison, the II was obtained from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) BV database. PV impact was analyzed using the absolute difference between IP and II (Δ IP-II ). 798,800 observations from 17,082 adult patients were analyzed. For most biomarkers, the IP (mean=1.99, range=0.55-8.03) differed from the II (mean=0.54, range=0.27-0.86). Lowest IPs were for creatinine (IP=0.55, Δ IP-II =0.28) and bilirubin (IP=1.05, Δ IP-II =0.24). Highest IPs were for aspartate transaminase (IP=4.56, Δ IP-II =4.13) and creatine kinase (IP=8.03, Δ IP-II =7.60). Hormones and proteins exhibited high PV impact (Δ IP-II >1.0). Differences between variational estimates that only account for healthy states (II-BV) and those that consider healthy and unhealthy states (IP-BV+PV) vary widely among biomarkers, highlighting the differential impact of PV on their interpretation. For biomarkers where IP is high, the RI may be useful to identify unhealthy individuals. • Variation beyond the healthy state is different across biomarkers. • Considering the variation beyond the healthy state alters clinical interpretation. • Hormones, proteins exhibit highest impact from variation in the unhealthy state.
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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 | 0.099 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".