CANN2021 is a nationwide cohort of Norwegian high school students created with the aim of addressing emerging issues in epidemiology of cannabis use through the initial surveillance and examination of its correlates, causes and consequences.

Between 25 February 2021 and 10 April 2021, a core baseline sample of 3490 students (48% boys; 11th grade 35.5%, 12th grade 31.2%, 13th grade 33.3%) from 34 high schools in Norway anonymously completed comprehensive e-surveys assessing their cannabis-related involvement and experiences. A total of 1510 (43.3%) participants (45.8% boys; 11th grade 28.9%, 12th grade 31.1%, 13th grade 40.0%) provided identifying information and consented to administrative contact entailing individual-level linkages of their survey responses to their health and census data as recorded in various national registries since 2010, thus establishing the CANN2021 registry-linkages cohort.

The core baseline sample (N=3490) was largely representative of the Norwegian high school youth between the ages of 17 and 19 years, and as such of relevance to national surveillance needs. One in five (20.3%) reported having used cannabis at least once during their lifetime; of these, 40.9% consented to registry linkages.

E-survey data from the registry cohort will be linked at the individual level to health and administrative registries such as the Norwegian Patient Registry, Education, Crime, Income and Population Registry in 2025, 2029 and 2031. The retrospective and prospective linkages of baseline e-surveys with registry data can thus be used to address a range of epidemiological and public health questions, including examination of temporal associations between various types of early cannabis involvement and putative risk and protective factors, and subsequent health and social outcomes.

Cardiac surgery remains a high-risk procedure for bleeding despite advances in patient blood management. Conventional centrifugation-based autotransfusion devices primarily recover red blood cells, losing platelets and coagulation factors. The SAME autotransfusion device (i-SEP, Nantes, France) introduces an innovative filtration-based approach, recovering erythrocytes, leucocytes and platelets to enhance perioperative haemostasis. The main objective is to determine whether the filtration-based SAME device reduces significant perioperative bleeding compared with the centrifugation-based system in high-risk cardiac surgery patients.

The Centrifugation-based vs filtration-based intraOperative cell saLvage on qualiTy of peRioperAtive haemostasis iN cardiac surgEry (COLTRANE) trial is a multicentre, parallel-group, single-blinded, superiority-randomised clinical trial. Conducted over 19 months in 10 French hospitals, the study will target patients at high risk of bleeding undergoing on-pump cardiac surgery via sternotomy. A total of 570 patients (285 per group) are required to achieve 80% statistical power for detecting clinically significant differences. Eligible patients will be randomised to either a centrifugation-based or filtration-based autotransfusion group. Both groups will follow standardised perioperative and cardiopulmonary bypass management, with the devices used only intraoperatively. The primary outcome is the proportion of patients with clinically significant perioperative bleeding defined as classes 2 to 4 of the Universal Definition of Perioperative Bleeding. The secondary outcomes include device efficiency and safety, perioperative haemostasis, lengths of intensive care unit and hospital stays, early postoperative morbidity and 30-day all-cause mortality. Ancillary studies will be performed to evaluate cell recovery and washing performance, the viscoelastic properties of retransfused blood (Quantra Qplus; Stago, Asnières-sur-Seine, France), and the effect of salvaged leucocytes on postoperative inflammation and immune function.

This trial has received a favourable opinion from the Committee for the Protection of Persons and authorisation from the French authorities (Comité de protection des personnes Nord Ouest, IDRCB: 2023-A02566-39). Protocol V.1.1 was approved on 22 January 2024. The trial is registered on ClinicalTrials.gov (NCT06425614). The findings will be disseminated through oral communications at national and international scientific meetings and peer-reviewed journal publications. Individual participant data will be made available on reasonable request to qualified researchers, following review and approval by the study sponsor and ethics committee.

ClinicalTrials.gov, NCT06425614.

Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI’s capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy.

This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices.

Data collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).