The use of serial patient blood gas, electrolyte and glucose results to derive biologic variation: a new tool to assess the acceptability of intensive care unit testing
Bibliographic record
Abstract
BACKGROUND: Most estimates of biologic variation (s(b)) are based on periodically acquiring and storing specimens, followed by analysis within a single analytic run. We demonstrate for many intensive care unit (ICU) tests, only patient results need be statistically analyzed to provide reliable estimates of s(b). METHODS: Over 11 months, approximately 28,000 blood gas measurements (including electrolyte panels and glucose) were performed on one of two Radiometer ABL800 FLEX analyzers (Radiometer, Copenhagen, Denmark) from 1676 ICU patients. We tabulated the measurements of paired intra-patient blood samples drawn within 24 h of each other. After removal of outliers, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in 2-h intervals: 0-2 h, 2-4 h, 4-6 h, … 20-22 h and 22-24 h. The SDDs were then regressed against the time intervals of 2-14 h; extrapolation to zero time represents the sum of s(b) and short-term analytic variation (s(a)). RESULTS: Substitution of experimentally derived analytic error permitted the calculation of coefficient of variation (biologic) (CV(b)) (100 s(b)/mean): pH, 0.3%; pCO(2), 5.7%; pO(2), 13%; Na(+), 0.6%; K(+), 4.8%; Cl(-), 0.8%; HCO(3)(-), 3.2%; iCa(++), 2.4%; and glucose, 10.3%. The CV(b) of the electrolytes very closely matches the lowest estimates obtained in the usual manner. CONCLUSIONS: Derivation of the ratio of biologic to analytic variation indicates that the ABL800 is extremely suitable for ICU testing. This analysis should be extended to other point of care instrument systems.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".