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A careful design for refractive index sensor can yield a sensitivity of 1445 nm/refractive index unit (RIU) and a figure of merit (FOM) of 28.8.
Using 7 as a cutoff to predict an individual as a patient would yield a sensitivity of 64.8% and a specificity of 65%.
In the blinded test set, the results yield a sensitivity of 75.0% and a specificity of 83.5%.
The threshold of 88 cm yielded 76% sensitivity (specificity 63%) whilst a threshold of 92 cm would yield a sensitivity of under 70% but a specificity of 75%.
Furthermore, using maximal dependence decomposition (MDD) clustering can enhance the performance of the model on a fivefold cross-validation evaluation to yield a sensitivity of 0.863, specificity of 0.885, accuracy of 0.880, and MCC of 0.706.
For this model, hospitalizing all patients with a probability of ACS of at least 8.6%, corresponding to 65% of all patients, would yield a sensitivity of 95% and a specificity of 43%.
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In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%.
These results yielded a sensitivity of 68% and specificity of 73%.
A STAIN threshold of 1470 yielded a sensitivity of 0.66 and specificity of 0.96.
Indeed, the multivariate model yielded a sensitivity of 85.7% and a specificity of 86.1%, respectively.
This nanosensor yields a sensitivity of ∼800 nm/RIU and a figure of merit of about ∼1.35×104, which can find widely applications in the plasmonic nano-sensing area.
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