Application of 3-D Δ Check Graphs to HbA<sub>1c</sub>Quality Control and HbA<sub>1c</sub>Utilization
Bibliographic record
Abstract
Delta checking is a laboratory information system (LIS)-based tool that detects patient and laboratory quality control errors. By using hemoglobin A1c (HbA1c) data, we developed a novel approach to summarizing and presenting patient Delta values to address limitations of current Delta check algorithms. Delta values were calculated from intrapatient pairs of HbA1c (n = 55,327) measured during 2 years in a single referral or a university hospital laboratory. Three-dimensional Delta-time (DeltaT) and percentile limit graphs were constructed. Cumulative distribution function analysis was used to explore clinical utilization. The DeltaT graphs showed that HbA1c Delta values increase asymmetrically over time. Although the 2.5 to 97.5 and 5.0 to 95.0 percentile Delta check limits were similar for both sites, the referral laboratory's 0.5 to 99.5 percentile limits were wider. For acute patient care environments, we recommend limits of -3.5% and 1.8% for measurements between 0 and 60 days and -4.0% and 2.0% for measurements between 60 and 120 days. For the outpatient environment, we recommend limits of -4.2% and 2.1% and 5.0% and 2.5% for measurements between 0 and 60 days and 60 and 120 days, respectively.Delta checking can be significantly improved with customization of limits set by population and interobservation period. Because LIS systems are incapable of these customizations, customers must become advocates for these modifications.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".