Background Previous systematic reviews have assessed the prevalence and odds ratio (OR) of depression for patients with psoriatic disease. Due to probable bidirectional effects, prevalence and prevalence ORs are difficult to interpret. No prior reviews have quantified the relative risk (RR) of depression following a diagnosis of psoriatic disease. Objective To estimate the RR of depression in individuals with psoriasis and in psoriatic arthritis (PsA), clear-to-moderate psoriasis, and moderate-to-severe psoriasis subgroups. Methods Observational studies investigating the risk of depression in adults with psoriatic disease were systematically searched for in Medline, EMBASE, PsycINFO, and CINAHL databases; 4989 unique references were screened. Studies that reported measures of incident depression in psoriasis patients were included. Thirty-one studies were included into the systematic review, of which 17 were meta-analyzed. Random effects models were employed to synthesize relevant data. Sources of heterogeneity were explored with subgroup analysis and meta-regression. Results Nineteen studies were included in meta-analyses. The pooled RR of depression in psoriasis patients compared to nonpsoriasis controls was 1.48 (95% CI: 1.16-1.89). Heterogeneity was high (I2 = 99.8%). Subgroup analysis and meta-regression did not indicate that PsA status or psoriasis severity (clear-to-mild, moderate-to-severe) were sources of heterogeneity. No evidence of publication bias was found. Conclusions This review demonstrates that the risk of depression is greater in patients with psoriasis and PsA. Future research should focus on developing strategies to address the mental health needs of this patient population for depression, including primary prevention, earlier detection, and treatment strategies.

Purpose: Breast cancer incidence among younger women (under age 50) has increased over the past 25 years, yet little is known about the etiology among this age group. The objective of this study was to investigate relationships between modi able and non-modi able risk factors and early-onset breast cancer among three prospective Canadian cohorts.Methods: A matched case-control study was conducted using data from Alberta's Tomorrow Project, BC Generations Project, and the Ontario Health Study. Participants diagnosed with breast cancer before age 50 were identi ed through provincial registries and matched to three control participants of similar age and follow-up. Conditional logistic regression was used to examine the association between factors and risk of early-onset breast cancer.Results: In total, 609 cases and 1,827 controls were included. A body mass index ≥30kg/m 2 was associated with a lower risk of early-onset breast cancer (OR=0.65; 95% CI: 0.47-0.90), while a waist circumference ≥88 cm was associated with an increased risk (OR=1.40; 95% CI: 1.06-1.84). A reduced risk was found for women with ≥2 pregnancies (OR=0.80; 95% CI: 0.64-1.00) and a rst-degree family history of breast cancer was associated with an increased risk (OR=2.06; 95% CI: 1.54-2.75).Conclusions: In this study, measures of adiposity, pregnancy history, and familial history of breast cancer are important risk factors for early-onset breast cancer. Evidence was insu cient to conclude if smoking, alcohol intake, fruit and vegetable consumption, and physical activity are meaningful risk factors. The results of this study could inform targeted primary and secondary prevention for early-onset breast cancer.

Background With the growing excitement of the potential benefits of using machine learning and artificial intelligence in medicine, the number of published clinical prediction models that use these approaches has increased. However, there is evidence (albeit limited) that suggests that the reporting of machine learning–specific aspects in these studies is poor. Further, there are no reviews assessing the reporting quality or broadly accepted reporting guidelines for these aspects. Objective This paper presents the protocol for a systematic review that will assess the reporting quality of machine learning–specific aspects in studies that use machine learning to develop clinical prediction models. Methods We will include studies that use a supervised machine learning algorithm to develop a prediction model for use in clinical practice (ie, for diagnosis or prognosis of a condition or identification of candidates for health care interventions). We will search MEDLINE for studies published in 2019, pseudorandomly sort the records, and screen until we obtain 100 studies that meet our inclusion criteria. We will assess reporting quality with a novel checklist developed in parallel with this review, which includes content derived from existing reporting guidelines, textbooks, and consultations with experts. The checklist will cover 4 key areas where the reporting of machine learning studies is unique: modelling steps (order and data used for each step), model performance (eg, reporting the performance of each model compared), statistical methods (eg, describing the tuning approach), and presentation of models (eg, specifying the predictors that contributed to the final model). Results We completed data analysis in August 2021 and are writing the manuscript. We expect to submit the results to a peer-reviewed journal in early 2022. Conclusions This review will contribute to more standardized and complete reporting in the field by identifying areas where reporting is poor and can be improved. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020206167; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=206167 International Registered Report Identifier (IRRID) RR1-10.2196/30956

Early-onset diagnosis, defined by age <40 years, has historically been associated with inferior outcomes in breast cancer. Recent evidence suggests that this association is modified by molecular subtype. We performed a systematic review and meta-analysis of the literature to synthesize evidence on the association between early-onset diagnosis and clinical outcomes in triple-negative breast cancer (TNBC). Studies comparing the risk of clinical outcomes in non-metastatic TNBC between early-onset patients and later-onset patients (≥40 years) were queried in Medline and EMBASE from inception to February 2023. Separate meta-analyses were performed for breast cancer specific survival (BCSS), overall survival (OS), and disease-free survival (DFS), locoregional recurrence-free survival (LRRFS), distant recurrence-free survival (DRFS), and pathological complete response (pCR). In total, 7581 unique records were identified, and 36 studies satisfied inclusion criteria. The pooled risk of any recurrence was significantly greater in early-onset patients compared to later-onset patients. Better BCSS and OS were observed in early-onset patients relative to later-onset patients aged >60 years. The pooled odds of achieving pCR were significantly higher in early-onset patients. Future studies should evaluate the role of locoregional management of TNBC and the implementation of novel therapies such as PARP inhibitors in real-world settings, and whether they improve outcomes.