Diurnal rhythm in clinical chemistry: An underrated source of variation
Bibliographic record
Abstract
Diurnal rhythm complicates the acquisition of clinical laboratory samples by adding a predictable time-dependent component to the pre-analytical variation that, if not taken into account, degrades the clinical utility of results on certain analytes. Here we performed a systematic review of the literature and identified, in 56 publications that met our minimum inclusion criteria, 30 analytes that undergo diurnal variation. We graded the quality of evidence of these publications using a 3-tier scoring system of our own formulation. The rigor of the experimental design and agreement varied considerably across studies. Analyte concentration oscillated considerably over the 24-h day–night cycle. The median zenith-to-nadir change relative to the nadir concentration (%ZNC) for the 30 analytes was 100%. To set the magnitude of diurnal variation into perspective, we assessed the fluctuation in analyte concentration throughout the 24-h period (diurnal variation (%CVdv)) relative to the day-to-day fluctuation determined at a fixed time during the 24-h period (within-subject biological variation (%CVw)). Then we divided the %CVdv by the published %CVw to obtain a novel metric termed the diurnal variation index (DVI). The median DVI for the 16 analytes examined was 2.0, underscoring that, for most analytes, the diurnal variation was larger than the published %CVw and highlighting the importance of adhering to protocols regulating the time of sampling when dealing with these analytes. Given that the %CVw is the basis of the reference change value (RCV) and several quality metrics such as the Sigma metric (based on total allowable error), failure to regulate the time of sample collection will compromise these %CVw-based targets. We also provide examples where failure to regulate the time of collection of diurnally changing analytes compromises their diagnostic utility. Nevertheless, for the most part, websites of major laboratories in the USA and Europe do not consistently stipulate collection at specified time junctures for the majority of the analytes identified here.
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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 | 0.061 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".