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This article reviews the quasi-experimental literature on lead and fertility, lead and infant mortality, and lead and infant birth outcomes. It then discusses the relevance of these studies for policy. In contrast to the large amount of literature on children's blood lead levels and health and development outcomes, there are fewer studies on lead and fertility, lead and infant mortality, and lead and infant health, despite their social and economic importance. Although removal of lead in gasoline generated enormous public health benefits, lead exposure remains significant in both developed and developing countries. Thus, causal estimates from quasi-experimental studies of the relationships between lead and fertility, lead and infant mortality, and lead and infant health are critical for policy. Specifically, they can be used to generate estimates of benefits used in regulatory benefit–cost analyses. [ABSTRACT FROM AUTHOR]

We explore how access to modern hospitals and medicine affects mortality by leveraging efforts of the Duke Endowment to modernize hospitals in the early twentieth century. The Endowment helped communities build and expand hospitals, obtain state-of-the-art medical technology, attract qualified medical personnel, and refine management practices. We find that Duke support increased the size and quality of the medical sector, fostering growth in not-for-profit hospitals and high-quality physicians. Duke funding reduced both infant mortality—with larger effects for Black infants than White infants—and long-run mortality. Finally, we find that communities aided by Duke benefited more from medical innovations. (JEL I11, I12, J13, J15, L31, N32, O31) [ABSTRACT FROM AUTHOR]

Difference-in-difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DIDs are an average of simpler 2 × 2 DID subexperiments. [ABSTRACT FROM AUTHOR]

Aims: Structured reporting in pathology is not universally adopted and extracting elements essential to research often requires expensive and time-intensive manual curation. The accuracy and feasibility of using large language models (LLMs) to extract essential pathology elements, for cancer research is examined here., Methods: Retrospective study of patients who underwent pathology sampling for suspected hepatocellular carcinoma and underwent Ytrrium-90 embolisation. Five pathology report elements of interest were included for evaluation. LLMs (Generative Pre-trained Transformer (GPT) 3.5 turbo and GPT-4) were used to extract elements of interest. For comparison, a rules-based, regular expressions (REGEX) approach was devised for extraction. Accuracy for each approach was calculated., Results: 88 pathology reports were identified. LLMs and REGEX were both able to extract research elements with high accuracy (average 84.1%-94.8%)., Conclusions: LLMs have significant potential to simplify the extraction of research elements from pathology reporting, and therefore, accelerate the pace of cancer research., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available. A total of 9380 MRI slices containing sarcomas from 287 patients are available. Our MMNN accepts the entire 3D sarcoma volume from T1 and T2 MRIs and clinical variables. Gradient blending allows the clinical and image sub-networks to optimally converge without overfitting. Heat maps were generated to visualize the salient image features. Our MMNN outperformed all other models in predicting overall survival and the risk of distant metastases. The C-Index of our MMNN for overall survival is 0.77 and the C-Index for risk of distant metastases is 0.70. The provided heat maps demonstrate areas of sarcomas deemed most salient for predictions. Our multimodal neural network with gradient blending improves predictions of overall survival and risk of distant metastases in patients with soft tissue sarcoma. Future work enabling accurate subtype-specific predictions will likely utilize similar end-to-end multimodal neural network architecture and require prospective curation of high-quality data, the inclusion of genomic data, and the involvement of multiple centers through federated learning., (© 2024. The Author(s).)