Disrupting implicit bias: crowdsourced database highlights women experts in the social sciences #WomenAlsoKnowStuff

Women academics face inherent biases in the profession that limit career progression and influence. Emily Beaulieu and Kathleen Searles reflect on the extent of the gender gap in political science and how we might address this gap. One example is the #WomenAlsoKnowStuff website, a searchable database of women experts which has become a rallying cry, with hundreds of expert contributions and a steady stream of media attention

Accurate Dose Measurements Using Cherenkov Polarization Imaging

Purpose: Cherenkov radiation carries the potential of direct in-water dose measurements, but its precision is currently limited by a strong anisotropy. Taking advantage of polarization imaging, this work proposes a new approach for high accuracy Cherenkov dose measurements. Methods: Cherenkov produced in a 15x15x20 cm^3 water tank is imaged with a cooled CCD camera from four polarizer transmission axes [0{\deg}, 45{\deg}, 90{\deg}, 135{\deg}]. The water tank is positioned at the isocenter of a 5x5 cm^2, 6 MV photon beam. Using Malus' law, the polarized portion of the signal is extracted. Corrections are applied to the polarized signal following azimuthal and polar Cherenkov angular distributions extracted from Monte Carlo simulations. Percent depth dose and beam profiles are measured and compared with the prediction from a treatment planning system (TPS). Results: Corrected polarized signals on the central axis reduced deviations at depth from 20% to 0.8\pm1%. For the profile measurement, differences between the corrected polarized signal and the TPS calculations are 1\pm3% and 8\pm3% on the central axis and penumbra regions respectively. 29\pm1% of the Cherenkov signal was found to be polarized. Conclusions: This work proposes a novel polarization imaging approach enabling high precision water-based Cherenkov dose measurements. The method allows correction of the Cherenkov anisotropy within 3% on the beam central axis and in depth

Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific instructions. Here we report the performance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records (n = 271,081). The language model achieves strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allows the development of interpretable, complex phenotypes and subtypes. The Flan-T5 model achieves high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperforms a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this language modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: A retrospective cohort study

Background: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. Methods: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. Findings: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). Interpretation: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts.</p

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease

89 New Ultracool Dwarf Co-Moving Companions Identified With The Backyard Worlds: Planet 9 Citizen Science Project

We report the identification of 89 new systems containing ultracool dwarf companions to main sequence stars and white dwarfs, using the citizen science project Backyard Worlds: Planet 9 and cross-reference between Gaia and CatWISE2020. Thirty-two of these companions and thirty-three host stars were followed up with spectroscopic observations, with companion spectral types ranging from M7-T9 and host spectral types ranging from G2-M9. These systems exhibit diverse characteristics, from young to old ages, blue to very red spectral morphologies, potential membership to known young moving groups, and evidence of spectral binarity in 9 companions. Twenty of the host stars in our sample show evidence for higher order multiplicity, with an additional 11 host stars being resolved binaries themselves. We compare this sample's characteristics with those of the known stellar binary and exoplanet populations, and find our sample begins to fill in the gap between directly imaged exoplanets and stellary binaries on mass ratio-binding energy plots. With this study, we increase the population of ultracool dwarf companions to FGK stars by $\sim$42\%, and more than triple the known population of ultracool dwarf companions with separations larger than 1,000 au, providing excellent targets for future atmospheric retrievals.Comment: 61 pages, 11 figures, 11 tables. Accepted for publication in A

Genetic Disruption of Both Tryptophan Hydroxylase Genes Dramatically Reduces Serotonin and Affects Behavior in Models Sensitive to Antidepressants

The neurotransmitter serotonin (5-HT) plays an important role in both the peripheral and central nervous systems. The biosynthesis of serotonin is regulated by two rate-limiting enzymes, tryptophan hydroxylase-1 and -2 (TPH1 and TPH2). We used a gene-targeting approach to generate mice with selective and complete elimination of the two known TPH isoforms. This resulted in dramatically reduced central 5-HT levels in Tph2 knockout (TPH2KO) and Tph1/Tph2 double knockout (DKO) mice; and substantially reduced peripheral 5-HT levels in DKO, but not TPH2KO mice. Therefore, differential expression of the two isoforms of TPH was reflected in corresponding depletion of 5-HT content in the brain and periphery. Surprisingly, despite the prominent and evolutionarily ancient role that 5-HT plays in both vertebrate and invertebrate physiology, none of these mutations resulted in an overt phenotype. TPH2KO and DKO mice were viable and normal in appearance. Behavioral alterations in assays with predictive validity for antidepressants were among the very few phenotypes uncovered. These behavioral changes were subtle in the TPH2KO mice; they were enhanced in the DKO mice. Herein, we confirm findings from prior descriptions of TPH1 knockout mice and present the first reported phenotypic evaluations of Tph2 and Tph1/Tph2 knockout mice. The behavioral effects observed in the TPH2 KO and DKO mice strongly confirm the role of 5-HT and its synthetic enzymes in the etiology and treatment of affective disorders

A Worldwide Test of the Predictive Validity of Ideal Partner Preference-Matching

©American Psychological Association, [2024]. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: [ARTICLE DOI]”Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference-matching (i.e., do people positively evaluate partners who match versus mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered report—partnered with the Psychological Science Accelerator—uses a highly powered design (N=10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The “corrected pattern metric” that collapses across 35 traits revealed a zero-order effect of β=.19 and an effect of β=.11 when included alongside a normative preference-matching metric. Specific traits in the “level metric” (interaction) tests revealed very small (average β=.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men’s and (especially) women’s stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men’s stated preferences underestimated—and women’s stated preferences overestimated—revealed preferences. Implications for the literature on human mating are discussed.Unfunde