Appendix A. Detailed methods.

Detailed methods

Appendix D. Daphnia and chlorophyll data from a related experiment where cyanobacteria and microcystin were lower in abundance.

Daphnia and chlorophyll data from a related experiment where cyanobacteria and microcystin were lower in abundance

Developing an algorithm across integrated healthcare systems to identify a history of cancer using electronic medical records

OBJECTIVE: Tumor registries in integrated healthcare systems (IHCS) have high precision for identifying incident cancer but often miss recently diagnosed cancers or those diagnosed outside of the IHCS. We developed an algorithm using the electronic medical record (EMR) to identify people with a history of cancer not captured in the tumor registry to identify adults, aged 40-65 years, with no history of cancer. MATERIALS AND METHODS: The algorithm was developed at Kaiser Permanente Colorado, and then applied to 7 other IHCS. We included tumor registry data, diagnosis and procedure codes, chemotherapy files, oncology encounters, and revenue data to develop the algorithm. Each IHCS adapted the algorithm to their EMR data and calculated sensitivity and specificity to evaluate the algorithm\u27s performance after iterative chart review. RESULTS: We included data from over 1.26 million eligible people across 8 IHCS; 55 601 (4.4%) were in a tumor registry, and 44848 (3.5%) had a reported cancer not captured in a registry. The common attributes of the final algorithm at each site were diagnosis and procedure codes. The sensitivity of the algorithm at each IHCS was 90.65%-100%, and the specificity was 87.91%-100%. DISCUSSION: Relying only on tumor registry data would miss nearly half of the identified cancers. Our algorithm was robust and required only minor modifications to adapt to other EMR systems. CONCLUSION: This algorithm can identify cancer cases regardless of when the diagnosis occurred and may be useful for a variety of research applications or quality improvement projects around cancer care

Leveraging expertise and optimizing clinical research: Initial success of a pediatric epilepsy surgery collaborative

OBJECTIVE: Improve data-driven research to inform clinical decision-making with pediatric epilepsy surgery patients by expanding the Pediatric Epilepsy Research Consortium Epilepsy Surgery (PERC-Surgery) Workgroup to include neuropsychological data. This article reports on the process and initial success of this effort and characterizes the cognitive functioning of the largest multi-site pediatric epilepsy surgery cohort in the United States. METHODS: Pediatric neuropsychologists from 18 institutions completed surveys regarding neuropsychological practice and the impact of involvement in the collaborative. Neuropsychological data were entered through an online database. Descriptive analyses examined the survey responses and cognitive functioning of the cohort. Statistical analyses examined which patients were evaluated and if composite scores differed by domain, demographics, measures used, or epilepsy characteristics. RESULTS: Positive impact of participation was evident by attendance, survey responses, and the neuropsychological data entry of 534 presurgical epilepsy patients. This cohort, ages 6 months to 21 years, were majority White and non-Hispanic, and more likely to have private insurance. Mean intelligence quotient (IQ) scores were below to low average, with weaknesses in working memory and processing speed. Full-scale IQ (FSIQ) was lowest for patients with younger age at seizure onset, daily seizures, and magnetic resonance imaging (MRI) abnormalities. SIGNIFICANCE: We established a collaborative network and fundamental infrastructure to address questions outlined by the Epilepsy Research Benchmarks. There is a wide range in the age and IQ of patients considered for pediatric epilepsy surgery, yet it appears that social determinants of health impact access to care. Consistent with other national cohorts, this US cohort has a downward shift in IQ associated with seizure severity

Investigating Object Orientation Effects Across 18 Languages

Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Mental representation is often measured with the sentence-picture verification task, wherein participants first read a sentence that implies the object property (i.e., shape and orientation). Participants then respond to an image of an object by indicating whether it was an object from the sentence or not. Previous studies have shown matching advantages for shape, but findings concerning object orientation have not been robust across languages. This registered report investigated the match advantage of object orientation across 18 languages in nearly 4,000 participants. The preregistered analysis revealed no compelling evidence for a match advantage across languages. Additionally, the match advantage was not predicted by mental rotation scores. Overall, the results did not support current mental simulation theories