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Record W4416509914 · doi:10.1016/j.plabm.2025.e00511

The impact of pathological fluctuations versus biological variation on the interpretation of laboratory values

2025· article· en· W4416509914 on OpenAlexafffund
Ariel I. Mundo, Jean C. Emond, Vincent Cheung, Sahar Saeed, Philippe Desmarais, François Larivière, Pierre‐Olivier Hétu, Robert Goulden, Quôc Dinh Nguyên

Bibliographic record

VenuePractical Laboratory Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcGill University Health CentreUniversité de MontréalMcGill UniversityQueen's UniversityCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéFondation Mirella et Lino SaputoFonds de recherche du QuébecCanadian Institutes of Health ResearchUniversité Laval
KeywordsInterpretation (philosophy)Variation (astronomy)PathologicalDifferential (mechanical device)Perspective (graphical)

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.099
metaresearch head score (Gemma)0.290
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.290
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.091
GPT teacher head0.461
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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