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“Morbus Mediterraneus" and its impact on medical care in Germany: the intersection of pain and racism

Sergio R. Pérez Rosal, Sonya C. Faber, Monnica T. Williams

BMC Medical Ethics·2025

Abstract Background and objective In German healthcare, the colloquial term “Morbus Mediterraneus” is often used to dismiss pain complaints from racialized patients—particularly women of Mediterranean or non-White backgrounds—as exaggerated or dramatized. Although this label has no scientific basis, it perpetuates biased assumptions that lead to serious consequences, including undertreatment, misdiagnoses, and loss of trust in medical institutions. This paper provides a conceptual and literature-based analysis of how “Morbus Mediterraneus” reflects broader racist beliefs about pain tolerance, rooted in both colonial history and cultural norms in Germany. Methodology This is a conceptual study drawing on previously published qualitative findings, historical records, and contemporary literature regarding racism, pain perception, and healthcare disparities. We review how confusion around racial terminology in German discourse impedes recognition and measurement of systemic racism. We integrate scholarship from critical race theory, intersectionality, and structural competence to highlight the deep-seated impact of racial biases on clinical decision-making. Results Our analysis shows that “Morbus Mediterraneus” arises from a longstanding pattern of racialized medicine, where factors such as colonial research abuses, cultural misinterpretations of pain expression, and implicit provider biases converge. These biases systematically devalue the pain of racialized patients, especially women, and undermine patient–provider trust. Furthermore, we identify how gendered and racial stereotypes about emotional display and stoicism compound to create unique barriers to proper pain management. Conclusions Confronting biases tied to “Morbus Mediterraneus” requires integrated reforms across medical education, clinical practice, and policy. We recommend mandatory anti-racism and structural competence training, greater racial diversity among healthcare workers, standardized pain-assessment protocols, and improved data collection on race and ethnicity. By acknowledging colonial legacies and cultural norms that shape pain perception, German healthcare can better address systemic racism, ensure equitable pain management, and ultimately improve patient outcomes for all.

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Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer

Weiqian Huang, Ting Liu, Yichao Shen, Ziqing Xiang, Dong Wang, Wen Fu, Li Shao, Xianwen Yu, Weihua Ni, Yongqiang Zhou, Huan Liu, Ce Han, Xiance Jin, Ji Zhang

Journal of Applied Clinical Medical Physics·2025

Abstract Background Automatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans. Purpose To investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity‐modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization. Methods First, dose distributions were predicted using a three‐dimensional (3D) Fusion Residual Unet (F‐ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient‐specific quality assurance (PSQA). Results The predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V 30Gy of left femoral head ( p = 0.05), right femoral head ( p = 0.004), and small intestine ( p = 0.04). The V 45Gy of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%/3 mm and 3%/2 mm for VMAT and IMRT plans, respectively. Conclusions The proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.

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Information Extraction from Fiscal Documents using LLMs

Vikram Aggarwal, Jay Kulkarni, Aakriti Narang, Aditi Mascarenhas, Siddarth Raman, Ajay Shah, Susan Thomas

ideas.repec.org·2025

Downloadable! Large Language Models (LLMs) have demonstrated remarkable capabilities in text comprehension, but their ability to process complex, hierarchical tabular data remains underexplored. We present a novel approach to extracting structured data from multi-page government fiscal documents using LLM-based techniques. Applied to large annual fiscal documents from the State of Karnataka in India, our method achieves high accuracy through a multi-stage pipeline that leverages domain knowledge, sequential context, and algorithmic validation. Traditional OCR methods work poorly with errors that are hard to detect. The inherent structure of fiscal tables, with totals at each level of the hierarchy, allows for robust internal validation of the extracted data. We use these hierarchical relationships to create multi-level validation checks. We demonstrate that LLMs can read tables and also process document-specific structural hierarchies, offering a scalable process for converting PDF-based fiscal disclosures into research-ready databases. Our implementation shows promise for broader applications across developing country contexts.

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Tracing Multiphase Structure in the Circumgalactic Medium: Insights from Magnetohydrodynamic Turbulence Simulations

Rajsekhar Mohapatra, Alankar Dutta, Prateek Sharma

arXiv·2025

The circumgalactic medium (CGM) is the diffuse gas surrounding a galaxy's halo, and it plays a vital role in the galactic baryon cycle. However, its mass distribution across the virial phase and the cooler, denser atomic phase, remains uncertain. To investigate this, we perform high-resolution magnetohydrodynamic simulations of 0.125--8 kpc-scale representative patches of the CGM, with parameters informed by quasar absorption line observations. Our simulations resolve the cooling length (the minimum across all temperatures of $c_s t_{\rm cool}$, where $c_s$ is the sound speed and $t_{\rm cool}$ is the cooling time in isobaric conditions), allowing us to track the evolution of cold gas more accurately. We find that low-density CGM gas ($3\times10^{-4}$ cm$^{-3}$) cannot sustain cold gas below $10^4$ K for long, due to a large value of the ratio between the cooling to mixing time ($t_{\rm cool}/t_{\rm mix}$). In contrast, higher-density environments ($3\times10^{-3}~{\rm cm}^{-3}$) reach a turbulent multiphase steady state, with up to $50\%$ of the mass in the cold phase, occupying only about $1\%$ of the volume. To connect with large-volume cosmological simulations and small ${\rm pc}$-scale idealized simulations, we explore different box sizes (0.125--8 kpc) and identify a key scaling relation: simulations with similar $t_{\rm cool}/t_{\rm mix}$ exhibit comparable cold gas mass fractions and lifetimes. Importantly, we find that simply sub-sampling (reducing box-size) a small region from a large-volume simulation while maintaining a constant turbulent energy density injection rate from larger to smaller scales artificially shortens $t_\mathrm{mix}$, leading to inaccurate predictions for cold gas survival. This means that cold gas at small $\lesssim 10$ kpc scales arises in relatively dense, quiescent regions of the CGM rather than the turbulent ones undergoing cascade from large scales.

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Figuring Out Gas & Galaxies In Enzo (FOGGIE). XII. The Formation and Evolution of Extended HI Galactic Disks and Warps with a Dynamic CGM

Cameron W. Trapp, Molly S. Peeples, Jason Tumlinson, Brian W. O'Shea, Cassandra Lochhaas, Anna C. Wright, Britton D. Smith, Vida Saeedzadeh, Ayan Acharyya, Ramona Augustin, Raymond C. Simons

arXiv·2025

Atomic Hydrogen (HI) is an important component of gas in and around galaxies and forms extended disk-like structures well beyond the extent of starlight. Here we investigate the properties and evolution of extended HI disks that emerge in six Milky Way-mass galaxies using cosmological zoom-in simulations from the Figuring Out Gas & Galaxies in Enzo (FOGGIE) suite. We focus on the formation, evolution, and morphology of extended gaseous disks that emerge in all six systems. We find that median HI column densities drop sharply at the disk edge, with mean column densities outside the disk dominated by dense (NHI~10^19 cm-2), clumpy structures. All systems have significant misaligned features (warps or polar rings) at some point in their evolution; however, their frequencies, lifetimes, and origins vary significantly. We find that the morphology of the FOGGIE disks are correlated with properties of their Circumgalactic Medium (CGM). We classify these systems into two broad categories: those with CGMs that are Less Populated with HI and those with CGMs that are More Populated with HI. Both categories kinematically settle by z=0, but the Less Populated systems all form thin disks by z=0, while the More Populated systems do not. This classification is independent of disk and halo mass, implying the formation of a thin disk is influenced by local environmental factors. Our results indicate a connection between CGM content and disk formation that is not yet fully understood. A second paper investigates observational aspects of these structures.

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Accretion-Driven Turbulence in the Circumgalactic Medium

Roy Goldner, Jonathan Stern, Drummond Fielding, Claude-André Faucher-Giguère, Yakov Faerman, Aharon Kakoly

arXiv·2025

Both observations and hydrodynamic simulations suggest that turbulence is ubiquitous in the circumgalactic medium (CGM). We study the development and properties of CGM turbulence driven by accretion in halo masses of $10^{10}-10^{13}\,{\rm M}_\odot$ at redshifts $0\leq z\lesssim 2$, thus providing a baseline for additional turbulence driving processes such as galaxy feedback. Using analytic considerations and idealized hydrodynamical simulations we demonstrate that in halos with mass up to $\sim10^{12}\,{\rm M}_\odot$, even mild turbulent velocities near the virial radius of $σ_{\rm t}(R_{\rm vir})\sim 10\,{\rm km \, s^{-1}}$ are enhanced by the accretion process so that turbulent velocities are comparable to the virial velocity at inner CGM radii, with $σ_{\rm t}(0.1\,R_{\rm vir})\approx v_{\rm vir}\sim 100\,{\rm km \, s^{-1}}$. Rapid cooling at these inner radii further implies that thermal pressure support is small, and the gas is dominated by the cool and warm ($\sim10^4-10^5\,{\rm K}$) phases. Inner CGM energetics are thus dominated by turbulence, with gas density distributions and velocity structure functions similar to those seen in simulations of isothermal supersonic ISM turbulence, rather than those seen in subsonically turbulent stratified media such as the ICM. The gas accretion rate in these systems is regulated by the turbulence dissipation rate rather than by the cooling rate as in more massive halos. We argue that galaxy feedback is unlikely to qualitatively change our conclusions unless it continuously injects high specific energy material ($\gg v^2_{\rm vir}$) into the CGM. Such `turbulence-dominated' CGM can be identified in observations via the predicted wide lognormal ionization distributions and large velocity dispersions in UV absorption spectra, particularly in the inner CGM of $\sim L^\star$ and lower-mass halos.

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Best Practices for Biorisk Evaluations on Open-Weight Bio-Foundation Models

Boyi Wei, Zora Che, Nathaniel Li, Udari Madhushani Sehwag, Jasper Götting, Samira Nedungadi, Julian Michael, Summer Yue, Dan Hendrycks, Peter Henderson, Zifan Wang, Seth Donoughe, Mantas Mazeika

arXiv·2025

Open-weight bio-foundation models present a dual-use dilemma. While holding great promise for accelerating scientific research and drug development, they could also enable bad actors to develop more deadly bioweapons. To mitigate the risk posed by these models, current approaches focus on filtering biohazardous data during pre-training. However, the effectiveness of such an approach remains unclear, particularly against determined actors who might fine-tune these models for malicious use. To address this gap, we propose BioRiskEval, a framework to evaluate the robustness of procedures that are intended to reduce the dual-use capabilities of bio-foundation models. BioRiskEval assesses models' virus understanding through three lenses, including sequence modeling, mutational effects prediction, and virulence prediction. Our results show that current filtering practices may not be particularly effective: Excluded knowledge can be rapidly recovered in some cases via fine-tuning, and exhibits broader generalizability in sequence modeling. Furthermore, dual-use signals may already reside in the pretrained representations, and can be elicited via simple linear probing. These findings highlight the challenges of data filtering as a standalone procedure, underscoring the need for further research into robust safety and security strategies for open-weight bio-foundation models.

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Thought Branches: Interpreting LLM Reasoning Requires Resampling

Uzay Macar, Paul C. Bogdan, Senthooran Rajamanoharan, Neel Nanda

arXiv·2025

Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single sample is inadequate for understanding causal influence and the underlying computation. Though fully specifying this distribution is intractable, it can be understood by sampling. We present case studies using resampling to investigate model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In "agentic misalignment" scenarios, we resample specific sentences to measure their downstream effects. Self-preservation sentences have small causal impact, suggesting they do not meaningfully drive blackmail. Second, are artificial edits to CoT sufficient for steering reasoning? These are common in literature, yet take the model off-policy. Resampling and selecting a completion with the desired property is a principled on-policy alternative. We find off-policy interventions yield small and unstable effects compared to resampling in decision-making tasks. Third, how do we understand the effect of removing a reasoning step when the model may repeat it post-edit? We introduce a resilience metric that repeatedly resamples to prevent similar content from reappearing downstream. Critical planning statements resist removal but have large effects when eliminated. Fourth, since CoT is sometimes "unfaithful", can our methods teach us anything in these settings? Adapting causal mediation analysis, we find that hints that have a causal effect on the output without being explicitly mentioned exert a subtle and cumulative influence on the CoT that persists even if the hint is removed. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled CoT interventions.

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All Papers

“Morbus Mediterraneus" and its impact on medical care in Germany: the intersection of pain and racism

Pricing and constructing international government bond portfolios

Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer

The uncertain path to reward: Neural mechanisms of intolerance of uncertainty in reward processing

Information Extraction from Fiscal Documents using LLMs

Clinical technology advances driving in vivo CAR therapy

Tracing Multiphase Structure in the Circumgalactic Medium: Insights from Magnetohydrodynamic Turbulence Simulations

Figuring Out Gas & Galaxies In Enzo (FOGGIE). XII. The Formation and Evolution of Extended HI Galactic Disks and Warps with a Dynamic CGM

Accretion-Driven Turbulence in the Circumgalactic Medium

Best Practices for Biorisk Evaluations on Open-Weight Bio-Foundation Models

Writing With AI, About AI, and By AI · Issue 7.4, Fall 2025

Thought Branches: Interpreting LLM Reasoning Requires Resampling