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Interestingly, these relations also extended to treatment outcome; DAS28 remission was less likely among patients experiencing deviations due to toxicity, comorbidity, patient-related factors or other factors, whereas physician-related factors did not have any impact on long-term treatment outcome.
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Just over half (56.7%) of protocol deviations were categorised as non-permissible.> -wrap-foot>> -wrAs-foot> As more than one reason can contribute to a deviation, the total number of patients experiencing deviation is less than the total number of deviations.
In brief, objects moving close to the speed of light will experience deviations from the above formula due to the time dilation of special relativity which can be corrected for by introducing the Lorentz factor into the classical Doppler formula as follows (for motion solely in the line of sight): :1 + z = \left(1 + \frac{v}{c}\right) \gamma.
Accordingly, patients who experienced deviations due to toxicities (odds ratio (OR), 95% CI: 5.7 (3.0 to 10.7), P <0.0001), comorbidities (OR, 95% CI: 9.6 (4.8 to 21.2), P <0.0001) and patient-related reasons (OR, 95% CI: 3.6 (1.9 to 6.7), P <0.0001) were more likely to experience persistent or recurrent deviations.
Experienced Deviation from Normal State measured the experience of altered state of consciousness.
Patients with high baseline BMI and helplessness scores were more likely to experience protocol deviations.
When stratifying by the period of treatment, there was no difference during the first year of therapy, but patients treated according to the DAS28-based approach experienced more deviations during the second (P <0.001) and third years (P = 0.025).
In addition, the preemptive network modification considered here is clearly not practical in all cases; adaptation of the methodology to the control of networks that have already experienced first deviations will facilitate the analysis of network control in broader contexts.
Obviously, this effect is likely to be more predominant in matrices such as urine, in which the presence of salt influences the ionic strength, and resonances corresponding to certain molecules can experience larger deviations.
We now show how our model manages this problem; that is, it avoids voluntarily experiencing extreme homeostatic deviations and hence ensures that the animal does not voluntarily endanger its physiological integrity (simulations in Figure 6).
It should be pointed out that this delay does not significantly affect the discussion based on Eq. 6, since both adsorbates experience this deviation to the same extent.
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