Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.

The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).

We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.

European cooperation in Health Technology Assessment (HTA) requires joint assessments to be of high quality, providing findings transferable into national HTA report. To this aim, we piloted the combining of methodological guidance of EUnetHTA for Relative Effectiveness Assessment (REA), GRADE for selection/rating of outcomes and assessing quality of evidence, and Cochrane for Systematic Reviews, while carrying out a collaborative REA on Femtosecond Laser Assisted versus Standard Cataract Surgery.

While developing the collaborative REA, we used the three organizations’ handbooks, templates and tools for Scope, Project Plan (PP), Summary of Findings, Effectiveness (EFF) and Safety (SAF) domains. We structured the PP according to the EUnetHTA template and added detailed methods on EFF and SAF systematic reviews, as per Cochrane Handbook. For the Scope we convened a multidisciplinary panel for selection and rating of importance of outcomes and clinically significant difference, using the GRADEpro platform. We developed the complete report adopting the EUnetHTA REA Core Model. We used Cochrane's tool Revman to assess risk of bias of included studies for each outcome, and to carry out metanalyses. We applied the GRADE approach to assess quality of evidence for each outcome and to express level of certainty in the estimates. We used the Cochrane handbook's guidance for structuring a scientific abstract and a Plain Language Summary to integrate the Summary of Findings.

The PP resulted in a detailed scientific and operational protocol, receiving extensive and constructive internal and external peer review. Reporting of EFF and SAF domains followed EUnetHTA Assessment Elements while keeping the order of stakeholders' rating of outcomes’ importance. Graphic representation of risk of bias for each outcome contributed to immediacy of the data quality assessment and transparency of the judgement on certainty. The scientific abstract and the Plain Language Summary, facilitated the external dissemination of results.

Merging of the three most important methodological contributions in the field proved successful without altering the distinctive trait of the REA.