To evaluate the efficacy of immunotherapy for GTN treatment after methotrexate-resistance or in cases of multiresistant disease, through a systematic review, as well as to present the first 4 Brazilian cases of immunotherapy for GTN treatment. Three independent researchers searched five electronic databases (EMBASE, LILACS, Medline, CENTRAL and Web of Science), for relevant articles up to February/2023 (PROSPERO CRD42023401453). The quality assessment was performed using the Newcastle Ottawa scale for case series and case reports. The primary outcome of this study was the occurrence of complete remission. The presentation of the case reports was approved by the Institutional Review Board. Of the 4 cases presented, the first was a low-risk GTN with methotrexate resistance unsuccessfully treated with avelumab, which achieved remission with sequential multiagent chemotherapy. The remaining 3 cases were high-risk multiagent-resistant GTN that were successfully treated with pembrolizumab, among which there were two subsequent gestations, one of them with normal pregnancy and healthy conceptus. Regarding the systematic review, 12 studies were included, only one of them on avelumab, showing a 46.7% complete remission rate. The remaining 11 studies were on pembrolizumab, showing an 86.7% complete remission rate, regardless of tumor histology. Both immunotherapies showed good tolerability, with two healthy pregnancies being recorded: one after avelumb and another after pembrolizumab. Immunotherapy showed effectiveness for GTN treatment and may be especially useful in cases of high-risk disease, where pembrolizumab achieves a high therapeutic response, regardless of the histological type, and despite prior chemoresistance to multiple lines of treatment.

Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.

To assess the performance of pembrolizumab for the treatment of Gestational Trophoblastic Neoplasia (GTN). The Medical Subject Headings related to immunotherapy/pembrolizumab and GTN were used alone or in combination to retrieve relevant articles. The authors searched in EMBASE, MEDLINE/PubMed, Elsevier's Scopus, and Web of Science until November/2024. The authors included any randomized controlled trials, cohort studies, case series, and case reports focusing on pembrolizumab treatment in GTN. Meta-analysis of proportions was carried out employing a random-effects model. The meta-analysis employed the inverse variance method, with the arcsine link function for the analysis of proportional data. All analyses were performed using Stata 18. For all analyses, a p-value < 0.05 indicated statistical significance. This study was registered on PROSPERO (CRD42023493329). A total of 550 studies were identified after a literature search among which 15 original studies were included in the systematic review and in the meta-analysis. Pembrolizumab induced complete sustained remission in 71.59% (95% CI 53.27‒84.78%; I² = 0.00%, H² = 1.00, p = 0.90) of cases. The subgroups meta-analysis showed pembrolizumab had similar performance, regardless of age (< 40 vs. ≥ 40-years-old, p = 0.38), GTN histopathology (Placental Site Trophoblastic Tumor [PSTT], Epithelioid Trophoblastic Tumor [ETT]/noninvasive mole/others versus invasive mole/choriocarcinoma, p = 0.48), time from diagnosis to the beginning of immunotherapy (< 4 vs. ≥ 4-years, p = 0.84), pembrolizumab combined with chemotherapy (yes vs. no, p = 0.66). Pembrolizumab seems an effective treatment for patients with high-risk GTN with chemoresistant or relapsed disease, including cases of PSTT/ETT, notwithstanding patient age, time to initiate immunotherapy and whether or not it was associated with chemotherapy.

Introduction: The present study aims to investigate whether mothers from Culturally and Linguistically Diverse (CALD) backgrounds present with higher levels of demoralization in comparison with their non-minority counterparts, and to explore potential correlations between demoralization and...

BACKGROUND Acute esophageal variceal bleeding (AEVB) is a critical complication in patients with cirrhosis, associated with high mortality despite advancements in management. Traditional prognostic scores often lack predictive accuracy in this context. AIM To develop, internally validate, and prospectively validate a machine learning (ML) model to predict 1-year mortality in patients with cirrhosis presenting with AEVB. METHODS A retrospective cohort of 94 patients treated between 2010 and 2016 was used to train ML models, incorporating 36 clinical, laboratory, and imaging variables. Four algorithms (generalized linear models, boosted generalized linear models, naive Bayes, random forests) were evaluated, and the best-performing model was prospectively validated in a cohort of 24 patients treated between 2017 and 2018. Performance metrics included the area under the curve (AUC), sensitivity, specificity, and calibration via Brier scores. Data preprocessing involved k-nearest neighbor imputation, one-hot encoding, and scaling. RESULTS The random forest model achieved the highest AUC (0.91, 95% confidence interval [CI]: 0.85-0.96) during internal validation and demonstrated robust performance in the prospective cohort (AUC 0.88, 95%CI: 0.80-0.94). Calibration was excellent, with a low Brier score (0.12). The model was deployed as an online prediction tool. CONCLUSION This ML model shows promise in improving mortality prediction for AEVB, potentially aiding timely clinical interventions and decision-making. Prospective validation underscores its generalizability and clinical utility. Future research should explore external validation in diverse settings.