To assess the factors influencing dentists’ choice of restorative materials for posterior restorations, with a particular emphasis on the perceived influence of social media on patient preferences among general dental practitioners in Palestine.

Cross-sectional web-based survey.

A total of 550 general dentists practising in Palestine were invited between May and December 2023 through convenience and snowball sampling via social media platforms; 390 responded, and 350 complete responses were included in the final analysis.

No specific intervention was applied; this was an observational, questionnaire-based study.

Dentists’ selection of restorative materials (composite, amalgam or high-viscosity glass-ionomer cement (HVGIC)) for posterior restorations in relation to tooth type, patient age, oral hygiene, moisture control, financial status and social media influence.

Descriptive statistics, ² tests and multinomial logistic regression were used to examine associations and control for potential confounders.

Material selection varied significantly by tooth type (p

Patient-related factors were the main determinants of material selection, whereas practitioner demographics played a minimal role. HVGICs are preferred for paediatric and elderly patients because of their suitability for age-specific clinical needs. The influence of social media, assessed in this study as dentists’ perceptions rather than direct measures of patient behaviour, underscores its growing role in shaping dentists’ impressions of patient expectations and highlights the importance of evidence-based guidance and public education to support patient-centred, clinically appropriate restorative decisions.

Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports.

The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions.

Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.