Patient knowledge and beliefs about treatment and medical mistrust are mutable factors associated with underuse of effective adjuvant therapies. Physicians may improve cancer care by ensuring that discussions about adjuvant therapy include a clear presentation of the benefits, not just the risks of treatment, and by addressing patient trust in and concerns about the medical system.

Rationale: Prior studies have shown an anticancer effect of metformin in patients with breast and colorectal cancer. It is unclear, however, whether metformin has a mortality benefit in lung cancer.Objectives: To compare overall survival of patients with diabetes with stage IV non-small cell lung cancer (NSCLC) taking metformin versus those not on metformin.Methods: Using data from the Surveillance, Epidemiology, and End Results registry linked to Medicare claims, we identified 750 patients with diabetes 65-80 years of age diagnosed with stage IV NSCLC between 2007 and 2009. We used propensity score methods to assess the association of metformin use with overall survival while controlling for potential confounders.Measurements and Main Results: Overall, 61% of patients were on metformin at the time of lung cancer diagnosis. Median survival in the metformin group was 5 months, compared with 3 months in patients not treated with metformin (P , 0.001). Propensity score analyses showed that metformin use was associated with a statistically significant improvement in survival (hazard ratio, 0.80; 95% confidence interval, 0.71-0.89), after controlling for sociodemographics, diabetes severity, other diabetes medications, cancer characteristics, and treatment.Conclusions: Metformin is associated with improved survival among patients with diabetes with stage IV NSCLC, suggesting a potential anticancer effect. Further research should evaluate plausible biologic mechanisms and test the effect of metformin in prospective clinical trials.

Background Patients who have had COVID-19 often report persistent symptoms after resolution of their acute illness. Recent reports suggest that vaccination may be associated with improvement in post-acute symptoms. We used data from a prospective cohort to assess differences in post-acute sequelae of COVID (PASC) among vaccinated vs. unvaccinated patients. Methods We used data from a cohort of COVID-19 patients enrolled into a prospective registry established at a tertiary care health system in New York City. Participants underwent a baseline evaluation before COVID-19 vaccines were available and were followed 6 months later. We compared unadjusted and propensity score–adjusted baseline to 6-month change for several PASC–related symptoms and measures: anosmia, respiratory (cough, dyspnea, phlegm, wheezing), depression, anxiety, post-traumatic stress disorder (PTSD; COVID-19-related and other trauma), and quality-of-life domains among participants who received vs. those who did not receive COVID-19 vaccination. Results The study included 453 COVID-19 patients with PASC, of which 324 (72%) were vaccinated between the baseline and 6-month visit. Unadjusted analyses did not show significant differences in the baseline to 6-month change in anosmia, respiratory symptoms, depression, anxiety, PTSD, or quality of life ( p > 0.05 for all comparisons) among vaccinated vs. unvaccinated patients. Similar results were found in propensity-adjusted comparisons and in secondary analyses based on the number of vaccine doses received. Conclusions Our findings suggest that COVID vaccination is not associated with improvement in PASC. Additional studies are needed to better understand the mechanisms underlying PASC and to develop effective treatments. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-022-07465-w.

SUMMARYIn this paper, an adaptive on-line parametric identiÿcation algorithm based on the variable trace approach is presented for the identiÿcation of non-linear hysteretic structures. At each time step, this recursive least-square-based algorithm upgrades the diagonal elements of the adaptation gain matrix by comparing the values of estimated parameters between two consecutive time steps. Such an approach will enforce a smooth convergence of the parameter values, a fast tracking of the parameter changes and will remain adaptive as time progresses. The e ectiveness and e ciency of the proposed algorithm is shown by considering the e ects of excitation amplitude, of the measurement units, of larger sampling time interval and of measurement noise. The cases of exact-, under-, over-parameterization of the structural model have been analysed. The proposed algorithm is also quite e ective in identifying time-varying structural parameters to simulate cumulative damage in structural systems.