Level-specific Comparison of 3D Navigated and Robotic Arm-Guided Screw Placement: An Accuracy Assessment of 1210 Pedicle Screws in Lumbar Surgery.

BACKGROUND CONTEXT: Robotic spine surgery, utilizing 3D imaging and robotic arms, has been shown to improve the accuracy of pedicle screw placement compared to conventional methods, although its superiority remains under debate. There are few studies evaluating the accuracy of 3D navigated versus robotic-guided screw placement across lumbar levels, addressing anatomical challenges to refine surgical strategies and patient safety. PURPOSE: This study aims to investigate the pedicle screw placement accuracy between 3D navigation and robotic arm-guided systems across distinct lumbar levels. STUDY DESIGN: A retrospective review of a prospectively collected registry PATIENT SAMPLE: Patients undergoing fusion surgery with pedicle screw placement in the prone position, using either via 3D image navigation only or robotic arm guidance OUTCOME MEASURES: Radiographical screw accuracy was assessed by the postoperative computed tomography (CT) according to the Gertzbein-Robbins classification, particularly focused on accuracy at different lumbar levels. METHODS: Accuracy of screw placement in the 3D navigation (Nav group) and robotic arm guidance (Robo group) was compared using Chi-squared test/Fisher's exact test with effect size measured by Cramer's V, both overall and at each specific lumbosacral spinal level. RESULTS: A total of 321 patients were included (Nav, 157; Robo, 189) and evaluated 1210 screws (Nav, 651; Robo 559). The Robo group demonstrated significantly higher overall accuracy (98.6 vs. 93.9%; P<0.001, V=0.25). This difference of no breach screw rate was signified the most at the L3 level (No breach screw: Robo 91.3 vs. 57.8%, P<0.001, V=0.35) followed by L4 (89.6 vs. 64.7%, P<0.001, V=0.28), and L5 (92.0 vs. 74.5%, P<0.001, V=0.22). However, screw accuracy at S1 was not significant between the groups (81.1 vs. 72.0%, V=0.10). CONCLUSION: This study highlights the enhanced accuracy of robotic arm-guided systems compared to 3D navigation for pedicle screw placement in lumbar fusion surgeries, especially at the L3, L4, and L5 levels. However, at the S1 level, both systems exhibit similar effectiveness, underscoring the importance of understanding each system's specific advantages for optimization of surgical complications.

Heatwave attribution based on reliable operational weather forecasts.

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2-50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10-50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Limited Improvement with Minimally Invasive Lumbar Decompression Alone for Degenerative Scoliosis with Cobb Angle over 20 Degrees: The Impact of Decompression Location.

STUDY DESIGN: Retrospective review of a prospectively collected multi-surgeon registry. OBJECTIVE: To evaluate the outcomes of minimally invasive (MI) decompression in patients with severe degenerative scoliosis (DS) and identify factors associated with poorer outcomes. SUMMARY OF BACKGROUND CONTEXT: MI decompression has gained widespread acceptance as a treatment option for patients with lumbar canal stenosis and DS. However, there is a lack of research regarding the clinical outcomes and the impact of MI decompression location in patients with severe DS exhibiting a Cobb angle exceeding 20 degrees. MATERIALS AND METHODS: Patients who underwent MI decompression alone were included and categorized into the DS or control groups based on Cobb angle (>20 degrees). Decompression location was labeled as "scoliosis-related" when the decompression levels were across or between end vertebrae, and "outside" when the operative levels did not include the end vertebrae. The outcomes including Oswestry Disability Index (ODI) were compared between the propensity score-matched groups for improvement and minimal clinical importance difference (MCID) achievement at ≥1 year postoperatively. Multivariable regression analysis was conducted to identify factors contributing to the non-achievement of MCID in ODI of the DS group at the ≥1 year timepoint. RESULTS: A total of 253 patients (41 DS) were included in the study. Following matching for age, gender, osteoporosis status, psoas muscle area, and preoperative ODI, the DS groups exhibited a significantly lower rate of MCID achievement in ODI (DS: 45.5% vs. control 69.0%, P=0.047). The "scoliosis-related" decompression (Odds ratio: 9.9, P=0.028) was an independent factor of non-achievement of MCID in ODI within the DS group. CONCLUSION: In patients with a Cobb angle>20 degrees, lumbar decompression surgery, even in the MI approach, may result in limited improvement of disability and physical function. Caution should be exercised when determining a surgical plan, especially when decompression involves the level between or across the end vertebrae. LEVEL OF EVIDENCE: 3.

The Impact of Posterior Intervertebral Osteophytes on Patient-Reported Outcome Measures After L5-S1 Anterior Lumbar Interbody Fusion and Transforaminal Lumbar Interbody Fusion.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: (1) To develop a reliable grading system to assess the severity of posterior intervertebral osteophytes and (2) to investigate the impact of posterior intervertebral osteophytes on clinical outcomes after L5-S1 decompression and fusion through anterior lumbar interbody fusion (ALIF) and minimally-invasive transforaminal lumbar interbody fusion (MIS-TLIF). BACKGROUND: There is limited evidence regarding the clinical implications of posterior lumbar vertebral body osteophytes for ALIF and MIS-TLIF surgeries and there are no established grading systems that define the severity of these posterior lumbar intervertebral osteophytes. PATIENTS AND METHODS: A retrospective analysis of patients undergoing L5-S1 ALIF or MIS-TLIF was performed. Preoperative and postoperative patient-reported outcome measures of the Oswestry Disability Index (ODI) and leg Visual Analog Scale (VAS) at 2-week, 6-week, 12-week, and 6-month follow-up time points were assessed. Minimal clinically important difference (MCID) for ODI of 14.9 and VAS leg of 2.8 were utilized. Osteophyte grade was based on the ratio of osteophyte length to foraminal width. "High-grade" osteophytes were defined as a maximal osteophyte length >50% of the total foraminal width. RESULTS: A total of 70 consecutive patients (32 ALIF and 38 MIS-TLIF) were included in the study. There were no significant differences between the two cohorts in patient-reported outcome measures or achievement of MCID for Leg VAS or ODI preoperatively or at any follow-ups. On multivariate analysis, neither the surgical approach nor the presence of high-grade foraminal osteophytes was associated with leg VAS or ODI scores at any follow-up time point. In addition, neither the surgical approach nor the presence of high-grade foraminal osteophytes was associated with the achievement of MCID for leg VAS or ODI at 6 months. CONCLUSION: ALIF and MIS-TLIF are both valid options for treating degenerative spine conditions and lumbar radiculopathy, even in the presence of high-grade osteophytes that significantly occupy the intervertebral foramen. LEVEL OF EVIDENCE: 3.

Methane and the Paris Agreement temperature goals.

Meeting the Paris Agreement temperature goal necessitates limiting methane (CH4)-induced warming, in addition to achieving net-zero or (net-negative) carbon dioxide (CO2) emissions. In our model, for the median 1.5°C scenario between 2020 and 2050, CH4 mitigation lowers temperatures by 0.1°C; CO2 increases it by 0.2°C. CO2 emissions continue increasing global mean temperature until net-zero emissions are reached, with potential for lowering temperatures with net-negative emissions. By contrast, reducing CH4 emissions starts to reverse CH4-induced warming within a few decades. These differences are hidden when framing climate mitigation using annual 'CO2-equivalent' emissions, including targets based on aggregated annual emission rates. We show how the different warming responses to CO2 and CH4 emissions can be accurately aggregated to estimate warming by using 'warming-equivalent emissions', which provide a transparent and convenient method to inform policies and measures for mitigation, or demonstrate progress towards a temperature goal. The method presented (GWP*) uses well-established climate science concepts to relate GWP100 to temperature, as a simple proxy for a climate model. The use of warming-equivalent emissions for nationally determined contributions and long-term strategies would enhance the transparency of stocktakes of progress towards a long-term temperature goal, compared to the use of standard equivalence methods. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Forecast-based attribution of a winter heatwave within the limit of predictability.

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The "storyline" attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.

Improved calculation of warming-equivalent emissions for short-lived climate pollutants.

Anthropogenic global warming at a given time is largely determined by the cumulative total emissions (or stock) of long-lived climate pollutants (LLCPs), predominantly carbon dioxide (CO2), and the emission rates (or flow) of short-lived climate pollutants (SLCPs) immediately prior to that time. Under the United Nations Framework Convention on Climate Change (UNFCCC), reporting of greenhouse gas emissions has been standardised in terms of CO2-equivalent (CO2-e) emissions using Global Warming Potentials (GWP) over 100-years, but the conventional usage of GWP does not adequately capture the different behaviours of LLCPs and SLCPs, or their impact on global mean surface temperature. An alternative usage of GWP, denoted GWP*, overcomes this problem by equating an increase in the emission rate of an SLCP with a one-off "pulse" emission of CO2. We show that this approach, while an improvement on the conventional usage, slightly underestimates the impact of recent increases in SLCP emissions on current rates of warming because the climate does not respond instantaneously to radiative forcing. We resolve this with a modification of the GWP* definition, which incorporates a term for each of the short-timescale and long-timescale climate responses to changes in radiative forcing. The amended version allows "CO2-warming-equivalent" (CO2-we) emissions to be calculated directly from reported emissions. Thus SLCPs can be incorporated directly into carbon budgets consistent with long-term temperature goals, because every unit of CO2-we emitted generates approximately the same amount of warming, whether it is emitted as a SLCP or a LLCP. This is not the case for conventionally derived CO2-e.

The many possible climates from the Paris Agreement's aim of 1.5 °C warming.

The United Nations' Paris Agreement includes the aim of pursuing efforts to limit global warming to only 1.5 °C above pre-industrial levels. However, it is not clear what the resulting climate would look like across the globe and over time. Here we show that trajectories towards a '1.5 °C warmer world' may result in vastly different outcomes at regional scales, owing to variations in the pace and location of climate change and their interactions with society's mitigation, adaptation and vulnerabilities to climate change. Pursuing policies that are considered to be consistent with the 1.5 °C aim will not completely remove the risk of global temperatures being much higher or of some regional extremes reaching dangerous levels for ecosystems and societies over the coming decades.

Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming.

Empirical evidence suggests that variations in climate affect economic growth across countries over time. However, little is known about the relative impacts of climate change on economic outcomes when global mean surface temperature (GMST) is stabilized at 1.5°C or 2°C warming relative to pre-industrial levels. Here we use a new set of climate simulations under 1.5°C and 2°C warming from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) project to assess changes in economic growth using empirical estimates of climate impacts in a global panel dataset. Panel estimation results that are robust to outliers and breaks suggest that within-year variability of monthly temperatures and precipitation has little effect on economic growth beyond global nonlinear temperature effects. While expected temperature changes under a GMST increase of 1.5°C lead to proportionally higher warming in the Northern Hemisphere, the projected impact on economic growth is larger in the Tropics and Southern Hemisphere. Accounting for econometric estimation and climate uncertainty, the projected impacts on economic growth of 1.5°C warming are close to indistinguishable from current climate conditions, while 2°C warming suggests statistically lower economic growth for a large set of countries (median projected annual growth up to 2% lower). Level projections of gross domestic product (GDP) per capita exhibit high uncertainties, with median projected global average GDP per capita approximately 5% lower at the end of the century under 2°C warming relative to 1.5°C. The correlation between climate-induced reductions in per capita GDP growth and national income levels is significant at the p < 0.001 level, with lower-income countries experiencing greater losses, which may increase economic inequality between countries and is relevant to discussions of loss and damage under the United Nations Framework Convention on Climate Change.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

The myriad challenges of the Paris Agreement.

The much awaited and intensely negotiated Paris Agreement was adopted on 12 December 2015 by the Parties to the United Nations Framework Convention on Climate Change. The agreement set out a more ambitious long-term temperature goal than many had anticipated, implying more stringent emissions reductions that have been under-explored by the research community. By its very nature a multidisciplinary challenge, filling the knowledge gap requires not only climate scientists, but the whole Earth system science community, as well as economists, engineers, lawyers, philosophers, politicians, emergency planners and others to step up. To kick start cross-disciplinary discussions, the University of Oxford's Environmental Change Institute focused its 25th anniversary conference upon meeting the challenges of the Paris Agreement for science and society. This theme issue consists of review papers, opinion pieces and original research from some of the presentations within that meeting, covering a wide range of issues underpinning the Paris Agreement.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Attribution of changes in precipitation patterns in African rainforests.

Tropical rainforests in Africa are one of the most under-researched regions in the world, but research in the Amazonian rainforest suggests potential vulnerability to climate change. Using the large ensemble of Atmosphere-only general circulation model (AGCM) simulations within the weather@home project, statistics of precipitation in the dry season of the Congo Basin rainforest are analysed. By validating the model simulation against observations, we could identify a good model performance for the June, July, August (JJA) dry season, but this result does need to be taken with caution as observed data are of poor quality. Additional validation methods have been used to investigate the applicability of probabilistic event attribution analysis from large model ensembles to a tropical region, in this case the Congo Basin. These methods corroborate the confidence in the model, leading us to believe the attribution result to be robust. That is, that there are no significant changes in the risk of low precipitation extremes during this dry season (JJA) precipitation in the Congo Basin. Results for the December, January, February dry season are less clear. The study highlights that attribution analysis has the potential to provide valuable scientific evidence of recent or anticipated climatological changes, especially in regions with sparse observational data and unclear projections of future changes. However, the strong influence of sea surface temperature teleconnection patterns on tropical precipitation provides more challenges in the set up of attribution studies than midlatitude rainfall.

Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000.

Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing. Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 1766, these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion (refs 5, 6). Although the flooding was deemed a 'wake-up call' to the impacts of climate change at the time, such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail to account fully for the complex hydrometeorology associated with flooding. Here we present a multi-step, physically based 'probabilistic event attribution' framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing, we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth-century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.

Cumulative carbon emissions, emissions floors and short-term rates of warming: implications for policy.

A number of recent studies have found a strong link between peak human-induced global warming and cumulative carbon emissions from the start of the industrial revolution, while the link to emissions over shorter periods or in the years 2020 or 2050 is generally weaker. However, cumulative targets appear to conflict with the concept of a 'floor' in emissions caused by sectors such as food production. Here, we show that the introduction of emissions floors does not reduce the importance of cumulative emissions, but may make some warming targets unachievable. For pathways that give a most likely warming up to about 4°C, cumulative emissions from pre-industrial times to year 2200 correlate strongly with most likely resultant peak warming regardless of the shape of emissions floors used, providing a more natural long-term policy horizon than 2050 or 2100. The maximum rate of CO(2)-induced warming, which will affect the feasibility and cost of adapting to climate change, is not determined by cumulative emissions but is tightly aligned with peak rates of emissions. Hence, cumulative carbon emissions to 2200 and peak emission rates could provide a clear and simple framework for CO(2) mitigation policy.

Greenhouse-gas emission targets for limiting global warming to 2 degrees C.

More than 100 countries have adopted a global warming limit of 2 degrees C or below (relative to pre-industrial levels) as a guiding principle for mitigation efforts to reduce climate change risks, impacts and damages. However, the greenhouse gas (GHG) emissions corresponding to a specified maximum warming are poorly known owing to uncertainties in the carbon cycle and the climate response. Here we provide a comprehensive probabilistic analysis aimed at quantifying GHG emission budgets for the 2000-50 period that would limit warming throughout the twenty-first century to below 2 degrees C, based on a combination of published distributions of climate system properties and observational constraints. We show that, for the chosen class of emission scenarios, both cumulative emissions up to 2050 and emission levels in 2050 are robust indicators of the probability that twenty-first century warming will not exceed 2 degrees C relative to pre-industrial temperatures. Limiting cumulative CO(2) emissions over 2000-50 to 1,000 Gt CO(2) yields a 25% probability of warming exceeding 2 degrees C-and a limit of 1,440 Gt CO(2) yields a 50% probability-given a representative estimate of the distribution of climate system properties. As known 2000-06 CO(2) emissions were approximately 234 Gt CO(2), less than half the proven economically recoverable oil, gas and coal reserves can still be emitted up to 2050 to achieve such a goal. Recent G8 Communiqués envisage halved global GHG emissions by 2050, for which we estimate a 12-45% probability of exceeding 2 degrees C-assuming 1990 as emission base year and a range of published climate sensitivity distributions. Emissions levels in 2020 are a less robust indicator, but for the scenarios considered, the probability of exceeding 2 degrees C rises to 53-87% if global GHG emissions are still more than 25% above 2000 levels in 2020.

Warming caused by cumulative carbon emissions towards the trillionth tonne.

Global efforts to mitigate climate change are guided by projections of future temperatures. But the eventual equilibrium global mean temperature associated with a given stabilization level of atmospheric greenhouse gas concentrations remains uncertain, complicating the setting of stabilization targets to avoid potentially dangerous levels of global warming. Similar problems apply to the carbon cycle: observations currently provide only a weak constraint on the response to future emissions. Here we use ensemble simulations of simple climate-carbon-cycle models constrained by observations and projections from more comprehensive models to simulate the temperature response to a broad range of carbon dioxide emission pathways. We find that the peak warming caused by a given cumulative carbon dioxide emission is better constrained than the warming response to a stabilization scenario. Furthermore, the relationship between cumulative emissions and peak warming is remarkably insensitive to the emission pathway (timing of emissions or peak emission rate). Hence policy targets based on limiting cumulative emissions of carbon dioxide are likely to be more robust to scientific uncertainty than emission-rate or concentration targets. Total anthropogenic emissions of one trillion tonnes of carbon (3.67 trillion tonnes of CO(2)), about half of which has already been emitted since industrialization began, results in a most likely peak carbon-dioxide-induced warming of 2 degrees C above pre-industrial temperatures, with a 5-95% confidence interval of 1.3-3.9 degrees C.

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models.

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.