PreciMind Analytical Performance Intelligence System
PM-APIS · Multi-framework TEa resolution · Sigma · QC decision
Analytical Parameters
Enter method performance data. TEa is resolved automatically from the analyte database.
Within-laboratory long-term imprecision. CLSI EP05.
Enter signed bias (%). Negative = underestimation.
About PM-APIS
PM-APIS resolves TEa ambiguity by computing sigma across multiple frameworks simultaneously — the CLIA PT criterion, and EFLM Biological Variation (minimum, desirable, optimal).
This answers: "Which TEa should I use?" and "How does TEa choice affect my analytical performance conclusion?"
CLIA values are US proficiency-testing acceptable-performance criteria used here as an analytical performance specification. A derived Sigma category does not itself establish regulatory compliance.
PM-APIS Ready
Select an analyte and enter CV and Bias to run the multi-framework analytical performance intelligence pipeline.
Multi-framework TEa
CLIA + 3 BV tiers
Sigma comparison
Best vs worst case
Decision intelligence
Action + risk level
QC strategy
Auto-generated rules
Clinical Evidence & References
Westgard JO (2016)
Six Sigma Quality Design and Control. 3rd ed. Westgard QC, Madison, WI.
Aarsand AK et al. (2021)
The EuBIVAS biological variation database. Clinical Chemistry 67(6):849–862.
Fraser CG (2001)
Biological Variation: From Principles to Practice. AACC Press, Washington DC.
CMS QSO-24-15-CLIA REVISED (2024-07-08)
US CLIA proficiency-testing acceptable-performance criteria (CMS-3355-F; implemented 2025-01-01). Used here as an analytical performance specification, not as a regulatory compliance determination.
CLSI EP05, 4th Edition (2025)
Evaluation of Precision of Quantitative Measurement Procedures.
CLSI EP23-A (2011)
Laboratory Quality Control Based on Risk Management. Provides risk-based QC design framework.
ISO 15189:2022 §7.3.7.2
Internal quality control.