Sentence examples for misclassification is more from inspiring English sources

Also, we suspect that this misclassification is more likely to undercount research in low publication countries.

The opposite misclassification is more likely: where patients with true RA-ILD at baseline were not reported to the register.

Nondifferential misclassification is more likely to result in bias toward the null than away from the null, but not always (Armstrong 1998; Steenland et al. 2000).

Misclassification is more likely in the participants with lower levels of exposure because of the larger number of results < LOD, caused in part by laboratory limitations related to small serum volumes (Needham et al. 2005).

Such misclassification is more likely to affect results for the first and second trimesters than the third because they are further from the date at which residence was obtained (i.e., date of delivery).

Blair et al. (2007) suggested that exposure measurement error and the resulting misclassification is more likely to be nondifferential by disease status in epidemiology studies and will most frequently result in false negatives through attenuation of effect estimates.

If a lower rate of true TDR was assumed or higher rate of resistance in truly experienced patients was assumed (data not shown), the relative effect of misclassification was more pronounced, and vice versa if a higher rate of true TDR or lower rate of resistance in truly experienced patients was assumed (data not shown).

We expect that a good classifier should give especially precise predictions at the top levels of the KEGG hierarchy, while at the bottom levels misclassifications are more tolerable.

Experiments done by Adinj et al. in [7] show that even with the best image representations, the misclassification rate is more than 20%.

COPD on death certificates is also probably underreported [ 34, 48], which may result in non-differential misclassification that is more pronounced in younger subjects.

The authors hypothesized that measurement error might explain their findings, suggesting that "larger aggregation improves the representativity of the exposure estimates by decreasing exposure misclassification, which is more profound when using individual stations vs. regional averages" [ 31].