E-norms at Ten: What a Decade Taught the Method About Deriving Reference Values from Patient Data
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Introduction/Aims
The e-norms method derives reference values from a laboratory’s own mixed patient data, without recruiting healthy volunteers. A decade of use exposed three things the original description did not address: a dependence on how finely measurements are recorded, a minimum sample size below which derived limits are systematically too permissive, and an extension of the method to engineered composite features. This work characterizes the first two and describes a corrected plateau rule.
Methods
Twenty-six sensory amplitude datasets (18 to 3,301 measurements), three sensory conduction velocity datasets, nine median-nerve datasets and a 2,000-measurement peroneal file were analyzed. Amplitudes were progressively rounded; velocities were made finer by adding noise of 0.05 m/s. Subsamples of 50 to 750 were drawn from thirteen large datasets on raw, logarithmic and square-root scales. The corrected rule was validated against expert visual plateau selection and against the plateau proportions reported in 2015, and limit stability was estimated by resampling.
Results
At n = 50, amplitudes as recorded returned 0.28 of the full-sample limit with 73.1% failing; rounded to whole numbers they returned 1.03 with none failing. Integer velocities returned 1.03 with no failures, but failed in all 300 draws once noise was added. Derived limits were systematically too low below 250 to 300 measurements. The corrected rule returned 1.00 with 0.1% failures and left integer velocities essentially unchanged.
Discussion
The dependence is intrinsic to locating the plateau from gaps between sorted values. Obtaining 250 normal studies means accumulating 530 to 1,250, depending on a laboratory’s normal rate.
