Explore the vast range of titles available.

Through regular interactions with their neighbors, diverse groups inhabiting areas along the Inner Asian Mountain Corridor during the Bronze Age formed dynamic interregional networks that saw the proliferation and persistence of shared material cultures over vast geographic areas. In this paper we advocate for ceramics analyses that combine both macro- and micro-scale technological studies alongside those of style, in order not to lose sight of the actual people who drove defining transformations in the Bronze Age. We present a petrographic study of pottery from the Zhetysu region, southeastern Kazakhstan, to examine diachronic technological traditions with a special focus on routines of selection and raw material processing. Our results demonstrate site-specific potting technologies as well as traits that transcend both time and space across episodes of high genetic turnover in the human population.

Aerial light detection and ranging (lidar) has emerged as a powerful technology for mapping urban archaeological landscapes, especially where dense vegetation obscures site visibility 1 , 2 . More recently, uncrewed aerial vehicle/drone lidar scanning has markedly improved the resolution of three-dimensional point clouds, allowing for the detection of slight traces of structural features at centimetres of detail across large archaeological sites, a method particularly useful in areas such as mountains, where rapid deposition and erosion irregularly bury and expose archaeological remains 3 . Here we present the results of uncrewed aerial vehicle–lidar surveys in Central Asia, conducted at two recently discovered archaeological sites in southeastern Uzbekistan: Tashbulak and Tugunbulak. Situated at around 2,000–2,200 m above sea level, these sites illustrate a newly documented geography of large, high-altitude urban centres positioned along the mountainous crossroads of Asia’s medieval Silk Routes (6th–11th century CE (Common Era) 4 , 5 . Although hidden by centuries of surface processes, our pairing of very-high-resolution surface modelling with semiautomated feature detection produces a detailed plan of monumental fortifications and architecture spanning 120 ha at Tugunbulak, thereby demonstrating one of the largest highland urban constellations in premodern Central Asia. Documentation of extensive urban infrastructure and technological production among medieval communities in Central Asia’s mountains—a crucial nexus for Silk Road trade networks 6 —provides a new perspective on the participation of highland populations in the economic, political and social formation of medieval Eurasia. Pairing of very-high-resolution surface modelling with semiautomated feature detection produces a detailed plan of monumental fortifications and architecture spanning 120 ha at Tugunbulak, Uzbekistan, demonstrating one of the largest highland urban constellations in premodern Central Asia.

Through regular interactions with their neighbors, diverse groups inhabiting areas along the Inner Asian Mountain Corridor during the Bronze Age formed dynamic interregional networks that saw the proliferation and persistence of shared material cultures over vast geographic areas. In this paper we advocate for ceramics analyses that combine both macro- and micro-scale technological studies alongside those of style, in order not to lose sight of the actual people who drove defining transformations in the Bronze Age. We present a petrographic study of pottery from the Zhetysu region, southeastern Kazakhstan, to examine diachronic technological traditions with a special focus on routines of selection and raw material processing. Our results demonstrate site-specific potting technologies as well as traits that transcend both time and space across episodes of high genetic turnover in the human population.

ObjectiveIn order to address the lack of data on the health and healthcare needs of trans and non-binary adults, NHS England includes questions asking about both gender and trans status in its surveys to support quality improvement programmes.We used self-reported data from the GP Patient Survey to answer the research question: what are the demographic characteristics, health conditions and healthcare experiences of trans and non-binary adults in England?Design/settingNationally representative, population-based cross-sectional survey in England with survey data collection from January to March 2021.Participants840 691 survey respondents including 6333 trans and non-binary adults.OutcomesWe calculated weighted descriptive statistics, and using logistic regression explored 15 long-term physical and mental health conditions, and 18 patient experience items, covering overall experience, access, communication and continuity.ResultsTrans and non-binary adults were younger, more likely to be from Asian, black, mixed or other ethnic groups and more likely to live in more deprived parts of the country. Age-specific patterns of long-term conditions were broadly similar among trans and non-binary adults compared with all other survey respondents, with some variation by condition. Overall, inequalities in long-term health conditions were largest for autism: OR (95% CI), 5.8 (5.0 to 6.6), dementia: 3.1 (2.5 to 3.9), learning disabilities: 2.8 (2.4 to 3.2) and mental health: 2.0 (1.9 to 2.2), with variation by age. In healthcare experience, disparities are much greater for interpersonal communication (OR for reporting a positive experience, range 0.4 to 0.7 across items) than access (OR range 0.8 to 1.2). Additionally, trans and non-binary adults report much higher preference for continuity 1.7 (1.6 to 1.8), with no evidence of any differences in being able to see or speak to a preferred general practitioner.ConclusionThis research adds up to date evidence about population demographics, health and healthcare needs to support healthcare improvement for trans and non-binary adults.

In this new conference volume from the National Academy of Social Insurance, experts offer differing views on what changes will, and must, occur to ensure the continuing viability of Social Security, retirement benefits, unemployment insurance, Medicare, and health security programs. The book opens with a general overview of how economic and political forces will shape the future of social insurance. In the chapters that follow, contributors discuss and debate a full range of related topics, including future Social Security investment returns, the changing face of private retirement plans, insuring longevity risk in pensions and Social Security, issues in unemployment insurance, long-term financing, governance, and markets for Medicare, and health care for the underserved and uninsured. Contributors include William C. Dudley (Goldman Sachs), Richard Berner (Morgan Stanley Dean Witter), Kilolo Kijakazi (Center on Budget and Policy Priorities), Fay Lomax Cook (Institute for Policy Research, Northwestern University), Lawrence Jacobs (University of Minnesota), Jack VanDerhei (Fox School of Business Management, Temple University) Craig Copeland (Employee Benefit Research Institute), Jeffery R. Brown (John F. Kennedy School of Government, Harvard), Janet Norwood (1993-96 Advisory Council on Unemployment Compensation), Marilyn Moon (Urban Institute), Sheila Burke (Smithsonian Institution and Kennedy School of Government, Harvard), Mark Schlesinger (Yale), Gerard Anderson (Johns Hopkins University), Lauren LeRoy (Grantmakers in Health), Ruth Riedel (Alliance Healthcare Foundation of San Diego), and Henrie M. Treadwell (W. K. Kellog Foundation¡¯s Community Voices).

Background There is substantial unexplained geographical and surgeon-to-surgeon variation in rates of surgery. One would expect surgeons to treat patients and themselves similarly based on best evidence and accounting for patient preferences. Questions/purposes (1) Are surgeons more likely to recommend surgery when choosing for a patient than for themselves? (2) Are surgeons less confident in deciding for patients than for themselves? Methods Two hundred fifty-four (32%) of 790 Science of Variation Group (SOVG) members reviewed 21 fictional upper extremity cases (eg, distal radius fracture, De Quervain tendinopathy) for which surgery is optional answering two questions: (1) What treatment would you choose/recommend: operative or nonoperative? (2) On a scale from 0 to 10, how confident are you about this decision? Confidence is the degree that one believes that his or her decision is the right one (ie, most appropriate). Participants were orthopaedic, trauma, and plastic surgeons, all with an interest in treating upper extremity conditions. Half of the participants were randomized to choose for themselves if they had this injury or illness. The other half was randomized to make treatment recommendations for a patient of their age and gender. For the choice of operative or nonoperative, the overall recommendation for treatment was expressed as a surgery score per surgeon by dividing the number of cases they would operate on by the total number of cases (n = 21), where 100% is when every surgeon recommended surgery for every case. For confidence, we calculated the mean confidence for all 21 cases per surgeon; overall score ranges from 0 to 10 with a higher score indicating more confidence in the decision for treatment. Results Surgeons were more likely to recommend surgery for a patient (44.2% ± 14.0%) than they were to choose surgery for themselves (38.5% ± 15.4%) with a mean difference of 6% (95% confidence interval [CI], 2.1%–9.4%; p = 0.002). Surgeons were more confident in deciding for themselves than they were for a patient of similar age and gender (self: 7.9 ± 1.0, patient: 7.5 ± 1.2, mean difference: 0.35 [CI, 0.075–0.62], p = 0.012). Conclusions Surgeons are slightly more likely to recommend surgery for a patient than they are to choose surgery for themselves and they choose for themselves with a little more confidence. Different perspectives, preferences, circumstantial information, and cognitive biases might explain the observed differences. This emphasizes the importance of (1) understanding patients’ preferences and their considerations for treatment; (2) being aware that surgeons and patients might weigh various factors differently; (3) giving patients more autonomy by letting them balance risks and benefits themselves (ie, shared decision-making); and (4) assessing how dispassionate evidence-based decision aids help inform the patient and influences their decisional conflict. Level of Evidence Level III, diagnostic study.

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.