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Abstract Extreme melt and rainfall events can induce temporary acceleration of Greenland Ice Sheet motion, leading to increased advection of ice to lower elevations where melt rates are higher. In a warmer climate, these events are likely to become more frequent. In September 2022, seasonally unprecedented air temperatures caused multiple melt events over the Greenland Ice Sheet, generating the highest melt rates of the year. The scale and timing of the largest event overwhelmed the subglacial drainage system, enhancing basal sliding and increasing ice velocities by up to ∼240% relative to pre‐event velocities. However, ice motion returned rapidly to pre‐event levels, and the speed‐ups caused a regional increase in annual ice discharge of only ∼2% compared to when the effects of the speed‐ups were excluded. Therefore, although late melt‐season events are forecast to become more frequent and drive significant runoff, their impact on net mass loss via ice discharge is minimal.

Objective: This is a randomized controlled trial (NCT03056157) of an enhanced adaptive disclosure (AD) psychotherapy compared to present-centered therapy (PCT; each 12 sessions) in 174 veterans with posttraumatic stress disorder (PTSD) related to traumatic loss (TL) and moral injury (MI). AD employs different strategies for different trauma types. AD-Enhanced (AD-E) uses letter writing (e.g., to the deceased), loving-kindness meditation, and bolstered homework to facilitate improved functioning to repair TL and MI-related trauma. Method: The primary outcomes were the Sheehan Disability Scale (SDS), evaluated at baseline, throughout treatment, and at 3- and 6-month follow-ups (Brief Inventory of Psychosocial Functioning was also administered), the Clinician-Administered PTSD Scale (CAPS-5), the Dimensions of Anger Reactions, the Revised Conflict Tactics Scale, and the Quick Drinking Screen. Results: There were statistically significant between-group differences on two outcomes: The intent-to-treat (ITT) mixed-model analysis of SDS scores indicated greater improvement from baseline to posttreatment in the AD-E group (d = 2.97) compared to the PCT group, d = 1.86; −2.36, 95% CI [−3.92, −0.77], t(1,510) = −2.92, p <.001, d = 0.15. Twenty-one percent more AD-E cases made clinically significant changes on the SDS than PCT cases. From baseline to posttreatment, AD-E was also more efficacious on the CAPS-5 (d = 0.39). These differential effects did not persist at follow-up intervals. Conclusion: This was the first psychotherapy of veterans with TL/MI-related PTSD to show superiority relative to PCT with respect to functioning and PTSD, although the differential effect sizes were small to medium and not maintained at follow-up. What is the public health significance of this article?: Warzone-related posttraumatic stress disorder (PTSD) is a highly multifarious clinical problem, partly because combat trauma can entail extensive traumatic loss and moral injury, either from personal transgressive acts or bearing witness to or being victimized by others' transgressions. Adaptive disclosure (AD) is an evidence-based psychotherapy that was designed to help service members and veterans with war-related PTSD. We applied lessons learned from previous research on AD and enhanced AD to better help war veterans with loss- and moral-injury-related PTSD. We compared the enhanced AD (AD-E) with present-centered therapy (PCT) in a clinical trial of 174 veterans with PTSD. We found AD-E to be superior to PCT with respect to helping veterans function better and in terms of reducing PTSD symptom burden. [ABSTRACT FROM AUTHOR]

We explored the application of a machine learning algorithm for the timely detection of potential abusive head trauma (AHT) using the first free-text note of an encounter and demographic information. First free-text physician notes and demographic information were collected for children under 5 years of age at a Level 1 Trauma Center. The control group, which included patients with head/neck injury, was compared to those with AHT diagnosed by the Child Protective Team. Differential scores accounted for words overrepresented in AHT patient vs. control notes. Sentiment scores were reflective of note positivity/negativity and subjectivity scores accounted for note subjectivity/objectivity. The composite scores reflected the patient's differential score modified by the subjectivity score. Composite, sentiment, and subjectivity scores combined with demographic information trained a Random Forest (RF) machine learning algorithm to predict AHT. Final composite scores with demographic information were highly associated with AHT in a test dataset. The control group included 587 patients and the test group included 193 patients. Combining composite scores with demographic information into the RF model improved AHT classification area under the curve (AUC) from 0.68 to 0.78, with an overall accuracy of 84%. Feature importance analysis of our RF model revealed that composite score, sentiment, age, and subjectivity were the most impactful predictors of AHT. The sentiment was not significantly different between control and AHT notes (p = 0.87), while subjectivity trended higher for AHT notes (p = 0.081). We conclude that a machine learning algorithm can recognize patterns within free-text notes and demographic information that aid in AHT detection in children. III. [ABSTRACT FROM AUTHOR]