Application of a Simulation Model to Estimate Treatment Error and Clinical Risk Derived from Point-of-Care International Normalized Ratio Device Analytic Performance
Bibliographic record
Abstract
BACKGROUND: In 2016, the Food and Drug Administration (FDA) proposed to enhance performance expectations for point-of-care testing (POCT) international normalized ratio (INR) devices relative to International Organization for Standardization (ISO) 17593:2007. The objective of the study was to estimate the frequency of warfarin dosing errors associated with a central laboratory INR method, a POCT INR method, and the proposed FDA performance goals. METHODS: A data set of INR results (n = 51912) from adult patients with INR ≤4 was used to assess the influence of adding assay imprecision and bias on warfarin dose decisions. The frequency of error in warfarin dose and size of error (≥1 or ≥2 dose categories) was compared using published assay specifications for the Instrumentation Laboratory ACL TOP® and the Roche Diagnostics CoaguChek® XS relative to the proposed FDA guidelines. RESULTS: The frequency of warfarin dose misclassification was largely influenced by bias and was not sensitive to assay imprecision. The central laboratory and POCT INR methods met the FDA performance specifications, had equal rates of ≥2 warfarin dose category error, and had statistically different rates of ≥1 warfarin dose category error in large samples (n >250). CONCLUSIONS: Simulation models are useful tools for evaluating POCT INR assay performance criteria required to achieve the proposed FDA guidelines. This simulation depicted how the Roche Diagnostics CoaguChek XS instrument meets the guideline.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".