OpenAssistant Conversations -- Democratizing Large Language Model Alignment

Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) greatly reduce the required skill and domain knowledge to effectively harness the capabilities of LLMs, increasing their accessibility and utility across various domains. However, state-of-the-art alignment techniques like RLHF rely on high-quality human feedback data, which is expensive to create and often remains proprietary. In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 complete and fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers. Models trained on OpenAssistant Conversations show consistent improvements on standard benchmarks over respective base models. We release our code and data under a fully permissive licence.Comment: Published in NeurIPS 2023 Datasets and Benchmark

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Bilateral calcified renal metastases from osteosarcoma

Calcified renal metastases from osteosarcoma are extremely rare. We present a case of a young female with osteosarcoma of the right ulna who developed late recurrence in the form of large metastatic calcified renal and pulmonary lesions. Review of the literature suggests that osteosarcoma metastases of the kidneys usually exhibit aggressive behaviour with poor prognosis. A brief review of the calcified renal metastases including the index case is presented

Comparison of helical and axial mode indirect computed tomographic venography in patients with pulmonary thromboembolism

Objective: To compare the helical and axial modes of indirect computed tomographic (CT) venography (CTV) for accuracy for diagnosing deep venous thrombosis (DVT) of the lower extremities as well as for their radiation burden in patients proven to have pulmonary thromboembolism (PTE) on CT pulmonary angiography (CTPA). Subjects and Methods: Of patients evaluated with CTPA for suspected acute PE, 20 of patients who were found to have PTE underwent both indirect CTV of the lower extremities and color Doppler examination. For indirect CTV, patients were randomly assigned to helical and axial modes. The CTV and Doppler findings were interpreted by two experienced radiologists who were blinded to the results of each other. Results: Out of total of 260 venous segments analyzed (130 venous segments each by helical or axial CTV), thrombi were seen in 43 venous segments (15 and 28 each by helical or axial CTV respectively). On comparison with Doppler, helical CTV had 82.35% sensitivity and 99.11% specificity, whereas axial CTV had 96.6% sensitivity and 100% specificity. The mean radiation dose was significantly higher for helical (1153.57 mgy.cm) as compared to axial mode CTV (806.28 mgy.cm) with P value of <0.0001. Conclusion: Axial CTV results in decreased radiation dose without significant change in the accuracy, as compared to helical CTV in the evaluation of DVT

Meta-analysis evaluating risk of hyperkalemia stratified by baseline MRA usage in patients with heart failure receiving SGLT2 inhibitors

Background: Both mineralocorticoid receptor antagonists (MRAs) and sodium-glucose co-transporter type 2 inhibitors (SGLT2is) have demonstrated beneficial reductions in cardiovascular outcomes. However, the risk of precipitating hyperkalemia with their concomitant usage remains unclear. Methods: MEDLINE and Cochrane were searched from inception through March 2022. Randomized controlled trials on patients with heart failure (HF) evaluating the effect of SGLT2is on clinical outcomes between MRA users and non-users were considered for inclusion. Outcomes of interest were mild and moderate/severe hyperkalemia, for which hazard ratios (HR) were pooled using a random effects model. Results: From the 972 articles retrieved from the initial search, three RCTs (n = 14,462 patients) were included in our meta-analysis. Pooled analysis demonstrated no significant difference in the incidence of mild hyperkalemia between MRA users (HR 0.82 [0.70-0.97]) and non-users (HR 0.95 [0.77-1.17]) (P-interaction = 0.28). The risk of severe hyperkalemia was significantly decreased in MRA users (HR 0.59 [0.44-0.78]; p = 0.0002; I2 = 0%) but not in non-users (HR 0.76 [0.56-1.02]; p = 0.07; I2 = 0%) (P-interaction = 0.22). Sensitivity analysis including patients with HF with reduced ejection fraction (HFrEF) revealed similar results across all subgroups, but no significant reduction in the incidence of mild hyperkalemia (HR 0.89 [0.76-1.04] was noted in MRA users with HFrEF. Conclusion: MRAs reduced the risk of mild or moderate/severe hyperkalemia, when added to SGLT2is. Future clinical trials should target scrupulous assessment of the risk of mild and moderate/severe hyperkalemia when used concomitantly with MRA

Automatic Local Effect of Window/Level on 3-D Scale-Space Ellipsoidal Filtering on Run-Off-Arteries from White Blood Contrast-Enhanced Magnetic Resonance Angiography

Pre-filtering is a critical step in 3-D segmentation of the blood vessel and its display. This paper presents the local effect of window/level over the 3-D scale-space approach for filtering the white blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to an isotropic volume, and then the window/level is automatically adjusted slice by slice and a composite volume is generated. Then, 3-D edges are generated using the separable Gaussian derivative convolution with known scales. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3-D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the non-vasculature and background structures, yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, SNR and CNR images. We compare the filtering results with and without the usage of the local effect of window/level over 3-D scalespace ellipsoidal filtering. We show that the automatic window /level is effective in detecting small vessels which are otherwise difficult to extrapolate them. The system was run over 20 patient studies from different parts of the body such as: brain, abdomen, kidney and knee/ankle. The computer program takes around 150 seconds of processing time per study for a study for a data size of ### ###, which includes the complete performance evaluation

Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review

Abstract—The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, this paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. This paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as “level sets without regularizers. ” This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as “level sets with regularizers. ” In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis. Index Terms—Cortex, deformable models, differential geometry, front, fuzzy, level sets, propagation, regularization, segmentation, stopping forces, topology. I