Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, ...making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979-2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January-September (but not October-December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.
Wind‐driven mixing and Ekman pumping driven by slow‐moving tropical cyclones (TCs) can bring nutrients to the euphotic zone, promoting phytoplankton blooms (TC‐PBs) observable by satellite remote ...sensing. We examine an exceptional (z‐score = 18–48) TC‐PB induced by category‐1 Cyclone Oma near the South Pacific island of Vanuatu in February 2019, the most extreme event in the observed satellite record of South Pacific surface Chlorophyll‐a (Chl‐a). Examining all 156 South Pacific TC since 1997, we identify a “hover” parameter derivable from storm track data correlated with post‐TC surface Chl‐a (r = 0.84). Using a data set of synthetic storm tracks, we show revisit times for South Pacific TC‐PBs are O(250) years and O(1,500) years for Oma‐scale TC‐PBs. The episodic, extreme, but consistent nature of such events means they may imprint on sediment records. If so, we show their signature could be used to reconstruct past TC variability assuming near‐stationarity of TC statistics.
Plain Language Summary
We demonstrate that a relatively weak Tropical Cyclone, Oma, produced the most extreme primary production event observed in the South Pacific satellite record of surface Chlorophyll‐a. We examined all 156 South Pacific tropical cyclones since 1997—finding 15 storms had blooms in their wake, although none compared to the response in the wake of Oma. Phytoplankton blooms require specific conditions for formation, namely that they “hover” in place over a long enough period of time, so that wind‐driven upwelling of nutrient‐rich deep water can enter the sunlit surface layers of the ocean and promote photosynthesis. We can characterize the “hovering” of cyclones using a simple parameter derived from location data alone, and so we examine a 10,000‐year synthetic data set of 96,000 South Pacific tropical cyclone tracks to explore the recurrence period of these blooms in current climate. We find that Oma‐type blooms recur only once in 1,500–2,000 years. If biological material from these extreme events reaches the sea floor, the rare but consistent nature of such blooms may be visible in the sedimentary record and help constrain past variability in tropical cyclones.
Key Points
An extreme (σ > 18) surface Chlorophyll‐a event was observed following Tropical Cyclone Oma passing near Vanuatu in 2019
South Pacific chlorophyll response is correlated to a “hover” parameter derivable from storm track data alone
Oma‐sized blooms recur on millennial and longer timescales, and may imprint on the sedimentary record
Recent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar ...puncture. We derive and validate a model to identify febrile infants ≤60 days of age at low risk for SBIs using supervised machine learning approaches.
We conducted a secondary analysis of a multicenter prospective study performed between December 2008 and May 2013 of febrile infants. Our outcome was SBI, (culture-positive urinary tract infection, bacteremia, and/or bacterial meningitis). We developed and validated 4 supervised learning models: logistic regression, random forest, support vector machine, and a single-hidden layer neural network.
A total of 1470 patients were included (1014 >28 days old). One hundred thirty-eight (9.3%) had SBIs (122 urinary tract infections, 20 bacteremia, and 8 meningitis; 11 with concurrent SBIs). Using 4 features (urinalysis, white blood cell count, absolute neutrophil count, and procalcitonin), we demonstrated with the random forest model the highest specificity (74.9, 95% confidence interval: 71.5%-78.2%) with a sensitivity of 98.6% (95% confidence interval: 92.2%-100.0%) in the validation cohort. One patient with bacteremia was misclassified. Among 1240 patients who received a lumbar puncture, this model could have prevented 849 (68.5%) such procedures.
We derived and internally validated a supervised learning model for the risk-stratification of febrile infants. Although computationally complex, lacking parameter cutoffs, and in need of external validation, this strategy may allow for reductions in unnecessary procedures, hospitalizations, and antibiotics while maintaining excellent sensitivity.
There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, ...approaches to explaining complex machine learning (ML) models are rarely informed by end-user needs and user evaluations of model interpretability are lacking in the healthcare domain. We used extended revisions of previously-published theoretical frameworks to propose a framework for the design of user-centered displays of explanations. This new framework served as the basis for qualitative inquiries and design review sessions with critical care nurses and physicians that informed the design of a user-centered explanation display for an ML-based prediction tool.
We used our framework to propose explanation displays for predictions from a pediatric intensive care unit (PICU) in-hospital mortality risk model. Proposed displays were based on a model-agnostic, instance-level explanation approach based on feature influence, as determined by Shapley values. Focus group sessions solicited critical care provider feedback on the proposed displays, which were then revised accordingly.
The proposed displays were perceived as useful tools in assessing model predictions. However, specific explanation goals and information needs varied by clinical role and level of predictive modeling knowledge. Providers preferred explanation displays that required less information processing effort and could support the information needs of a variety of users. Providing supporting information to assist in interpretation was seen as critical for fostering provider understanding and acceptance of the predictions and explanations. The user-centered explanation display for the PICU in-hospital mortality risk model incorporated elements from the initial displays along with enhancements suggested by providers.
We proposed a framework for the design of user-centered displays of explanations for ML models. We used the proposed framework to motivate the design of a user-centered display of an explanation for predictions from a PICU in-hospital mortality risk model. Positive feedback from focus group participants provides preliminary support for the use of model-agnostic, instance-level explanations of feature influence as an approach to understand ML model predictions in healthcare and advances the discussion on how to effectively communicate ML model information to healthcare providers.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Horvat and Pelletier discuss the cluster randomized clinical trial by Haskell et al which aimed to improve bronchiolitis management by deimplementing potentially unnecessary care, including the use ...of albuterol. Among the trial's findings was a significant increase in the proportion of children who did not receive albuterol.
Severe sepsis in immunocompromised children is associated with increased mortality. This paper describes the epidemiology landscape, clinical acuity, and outcomes for severe sepsis in pediatric ...intestinal (ITx) and multi‐visceral (MVTx) transplant recipients requiring admission to the pediatric intensive care unit (PICU). Severe sepsis episodes were retrospectively reviewed in 51 ITx and MVTx patients receiving organs between 2009 and 2015. Twenty‐nine (56.8%) patients had at least one sepsis episode (total of 63 episodes) through December 2016. Bacterial etiologies accounted for 66.7% of all episodes (n = 42), occurring a median of 122.5 days following transplant (IQR 59–211.8 days). Multidrug‐resistant organisms (MDROs) accounted for 73.8% of bacterial infections; extended spectrum beta‐lactamase producers, vancomycin‐resistant enterococcus, and highly‐resistant Pseudomonas aeruginosa were the most commonly identified. Increased mechanical ventilation and vasoactive requirements were noted in MDRO episodes (OR 3.03, 95% CI 1.09–8.46 and OR 3.07, 95% CI 1.09–8.61, respectively; p < .05) compared to non‐MDRO episodes. PICU length of stay was significantly increased for MDRO episodes (7 vs. 3 days, p = .02). Graft loss was 24.1% (n = 7) and mortality was 24.1% (n = 7) in patients who experienced severe sepsis. Further attention is needed for MDRO risk mitigation and modification of sepsis treatment guidelines to ensure MDRO coverage for this population.
In pediatric intestinal and multi‐visceral transplant recipients, MDROs account for a large majority of severe sepsis episodes related to bacterial infections and are associated with an increased need for mechanical ventilation, use of vasoactive medications, and length of intensive care unit stay.
The effect of the horizontal size of sea ice floes on sea ice melting is commonly formulated using the ratio between side and basal floe area. This leads to the conclusion that floe size is not ...important for sea ice evolution when floes exceed about 30 m. This paper considers a mutual interaction between floe size, ocean circulation, and melting. We find that lateral density gradients form at the boundaries of floes and drive ocean‐mixed‐layer instability and energetic eddies that spread from the ice edge. The resulting circulation mixes heat horizontally, melting floes near their edges. Idealized ocean model experiments show that the sea ice response is sensitive to floe size in the range of 1–50 km, considerably larger than previously assumed important, as smaller floes melt more rapidly per unit ice area. It is proposed that the role of eddies and floe size distribution should be incorporated into current climate models.
Key Points
Sea ice melting rates are sensitive to floe sizes in the range of 1–50 km, larger floes than previously assumed
Ocean eddies that develop due to density gradients at floe edges lead to enhanced melting of floes at their boundaries
Eddies therefore effectively melt floes laterally, with smaller floes melting more rapidly per unit of sea ice area
To identify trends in pediatric emergency department (ED) utilization following the COVID-19 pandemic.
We performed a cross-sectional study from 37 geographically diverse US children's hospitals. We ...included ED encounters between January 1, 2010 and December 31, 2020, transformed into time-series data. We constructed ensemble forecasting models of the most common presenting diagnoses and the most common diagnoses leading to admission, using data from 2010 through 2019. We then compared the most common presenting diagnoses and the most common diagnoses leading to admission in 2020 to the forecasts.
29,787,815 encounters were included, of which 1,913,085 (6.4%) occurred during 2020. ED encounters during 2020 were lower compared to prior years, with a 65.1% decrease in April relative to 2010–2019. In forecasting models, encounters for depression and diabetic ketoacidosis remained within the 95% confidence interval CI; fever, bronchiolitis, hyperbilirubinemia, skin/subcutaneous infections and seizures occurred within the 80–95% CI during the portions of 2020, and all other diagnoses (abdominal pain, otitis media, asthma, pneumonia, trauma, upper respiratory tract infections, and urinary tract infections) occurred below the predicted 95% CI.
Pediatric ED utilization has remained low following the COVID-19 pandemic, and below forecasted utilization for most diagnoses. Nearly all conditions demonstrated substantial declines below forecasted rates from the prior decade and which persisted through the end of the year. Some declines in non-communicable diseases may represent unmet healthcare needs among children. Further study is warranted to understand the impact of policies aimed at curbing pandemic disease on children.
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