The Radio to GeV Afterglow of GRB 221009A

GRB 221009A (z = 0.151) is one of the closest known long γ-ray bursts (GRBs). Its extreme brightness across all electromagnetic wavelengths provides an unprecedented opportunity to study a member of this still-mysterious class of transients in exquisite detail. We present multiwavelength observations of this extraordinary event, spanning 15 orders of magnitude in photon energy from radio to γ-rays. We find that the data can be partially explained by a forward shock (FS) from a highly collimated relativistic jet interacting with a low-density, wind-like medium. Under this model, the jet’s beaming-corrected kinetic energy (E K ∼ 4 × 1050 erg) is typical for the GRB population. The radio and millimeter data provide strong limiting constraints on the FS model, but require the presence of an additional emission component. From equipartition arguments, we find that the radio emission is likely produced by a small amount of mass (≲6 × 10−7 M ⊙) moving relativistically (Γ ≳ 9) with a large kinetic energy (≳1049 erg). However, the temporal evolution of this component does not follow prescriptions for synchrotron radiation from a single power-law distribution of electrons (e.g., in a reverse shock or two-component jet), or a thermal-electron population, perhaps suggesting that one of the standard assumptions of afterglow theory is violated. GRB 221009A will likely remain detectable with radio telescopes for years to come, providing a valuable opportunity to track the full lifecycle of a powerful relativistic jet

Using patient‐reported outcomes to understand the effectiveness of guideline‐concordant care for post‐traumatic stress disorder in clinical practice

RationaleIdentifying predictors of improvement amongst patients receiving routine treatment for post-traumatic stress disorder (PTSD) could provide information about factors that influence the clinical effectiveness of guideline-concordant care. This study builds on prior work by accounting for delivery of specific evidence-based treatments (EBTs) for PTSD while identifying potential predictors of clinical improvement using patient-reported outcomes measurement.MethodOur sample consisted of 2 643 US Department of Veterans Affairs (VA) outpatients who initiated treatment for PTSD between 2008 and 2013 and received at least four PTSD checklist (PCL) measurements over 12 weeks. We obtained PCL data as well as demographic, diagnostic, and health services use information from the VA corporate data warehouse. We used latent trajectory analysis to identify classes of patients based on PCL scores, then determined demographic, diagnostic, and treatment predictors of membership in each class.ResultsPatients who met our PCL-based inclusion criteria were far more likely than those who did not receive EBTs. We identified two latent trajectories of PTSD symptoms. Patients in the substantial improvement group (25.9%) had a mean decrease in PCL score of 16.24, whereas patients in the modest improvement group improved by a mean of 8.09 points. However, there were few differences between the groups, and our model to predict group membership was only slightly better than chance (area under the curve [AUC] = 0.55). Of the 64 covariates we tested, the only robust individual predictor of improvement was gender, with men having lower odds of being in the substantial improvement group compared with women (odds ratio [OR] 0.76; 95% confidence interval [CI] 0.58-0.96).ConclusionVA patients with PTSD can realize significant improvement in routine clinical practice. Although available medical records-based variables were generally insufficient to predict improvement trajectory, this study did indicate that men have lower odds of substantial improvement than women