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Background: Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system., Methods: Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes., Results: The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g. , intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model., Conclusions: While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g. , using ontological information to inform ML (as reported in the linked paper 1 )., Competing Interests: Competing interests: RW and SM are unpaid directors of the Unlocking Behaviour Change Community Interest Company., (Copyright: © 2024 West R et al.)

Introduction: Unilateral primary aldosteronism (UPA) is the most frequent surgically curable form of endocrine hypertension. Adrenalectomy is the cornerstone of treatment for UPA, but outcomes after surgery are variable. The cause of resistant hypertension after surgery is still a matter of debate. Our aim was to investigate cure rates after surgery and to evaluate preoperative factors that might influence the surgical outcome., Methods: Between 2000 and 2021, the charts of 71 Tunisian patients who underwent laparoscopic adrenalectomy for UPA were retrospectively reviewed. Preoperative medical records were collected and follow-up data (1-158 months) were registered. Antihypertensive medication doses were calculated using defined daily doses (DDD) and postoperative outcomes were assessed using the Primary Aldosteronism Surgical Outcome (PASO) criterion., Results: Of 91 enrolled patients, 71 (59% women, mean age 46 years, median length of follow-up 21 months) were suitable for evaluation. Thirty-four patients (48%) had complete clinical success according to the PASO criteria. The most relevant factors associated with complete clinical success on univariate analysis were: absence of diabetes (p= 0.007), low body mass index (BMI) (p= 0.001), lower preoperative DDD (p= 0.01), preoperatively controlled blood pressure (p= 0.024), higher plasma aldosterone to renin ratio (ARR) (p= 0.001), adenoma subtyping (p <0.001) and aldosteronoma resolution score (ARS) (p= 0.002). Multivariate regression analysis showed that the major predictors of complete clinical success were absence of diabetes (OR: 5.205), a BMI < 30 (OR: 4.930), a plasma ARR > 332 (OR: 4.554) and an ARS ≥ 3 (OR: 2.056)., Conclusion: Complete and partial clinical response rates were achieved in respectively 48 and 43% of cases. The main predictors of complete resolution of hypertension were absence of diabetes, low BMI, high plasma ARR and high ARS. Taking these factors into account may help identify patients at risk of persistent postoperative hypertension who may require long-term surveillance and medication., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Saadi, Bedoui, Zaghbib, Boussaffa, Mokaddem, Nacef, Ayed, Derouiche, Khiari, Chakroun and Ben Slama.)

Objective: Hypertension cure following adrenalectomy in unilateral primary aldosteronism is not guaranteed. Its likelihood is associated with pre-operative parameters, which have been variably combined in six different predictive scoring systems. The relative performance of these systems is currently unknown. The objective of this work was to identify the best performing scoring system for predicting hypertension cure following adrenalectomy for primary aldosteronism., Design: Retrospective analysis in a single tertiary referral centre., Patients: Eighty-seven adult patients with unilateral primary aldosteronism who had undergone adrenalectomy between 2004 and 2018 for whom complete data sets were available to calculate all scoring systems., Measurements: Prediction of hypertension cure by each of the six scoring systems., Results: Hypertension cure was achieved in 36/87 (41.4%) patients within the first post-operative year, which fell to 18/71 (25.4%) patients at final follow-up (median 53 months, P = .002). Analysis of receiver operating characteristic area under the curves for the different scoring systems identified a difference in performance at early, but not late, follow-up. For all systems, the area under the curve was lower at early compared with late follow-up and compared to performance in the cohorts in which they were originally defined., Conclusions: No single scoring system performed significantly better than all others when applied in our cohort, although two did display particular advantages. It remains to be determined how best such scoring systems can be incorporated into the routine clinical care of patients with PA., (© 2021 John Wiley & Sons Ltd.)

Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors that arising from the adrenal medulla or extra-adrenal autonomic ganglia. Traditionally, PPGL was classified as benign or malignant based on the presence of distant metastasis at the time of initial surgery. However, according to WHO 2017 Classification of Tumors of Endocrine Organs, all PPGL has metastatic potential. The term "metastatic" is used, replacing "malignant" in this group of tumors. The prediction of PPGL's metastatic potential is a clinical concern, although many relevant indicators such as genetics, histology, pathology and molecular biology markers have been proved to be related to the metastasis of PPGL, but none of them is 100% predictive; various types of prediction systems had been created, but previous studies had demonstrated that they still need to be validated in multicenter studies. There is no unified clinical standard to differentiate metastatic from non-metastatic and a highly effective prediction system is of urgent need. In this review, we summarized all reported prediction systems, including the PASS system, the GAPP system, the COPPs system and the ASES system. Additional potential indicators that related to metastatic PPGL were also introduced., Competing Interests: None., (AJCR Copyright © 2020.)