A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues

Overrides of Clinical Decision Support Alerts in Primary Care Clinics

Clinical Decision Support (CDS) systems can alert physicians about potential clinical risks and suggest suitable treatment alternatives at appropriate times in the health care process. We evaluated the frequency with which physicians overrode medication alerts and the override reasons provided. Data obtained from primary care practices affiliated with two Harvard teaching hospitals were downloaded. Physicians overrode more than half of CDS medication alerts, with formulary, age-based, and renal substitutions the most likely. Many drug-drug and drug-allergy interactions overridden had the potential to cause patient harm

High resolution simulation of recent Arctic and Antarctic stratospheric chemical ozone loss compared to observations

Simulations of polar ozone losses were performed using the three-dimensional high-resolution (1° × 1°) chemical transport model MIMOSA-CHIM. Three Arctic winters 1999–2000, 2001–2002, 2002–2003 and three Antarctic winters 2001, 2002, and 2003 were considered for the study. The cumulative ozone loss in the Arctic winter 2002–2003 reached around 35% at 475K inside the vortex, as compared to more than 60% in 1999–2000. During 1999–2000, denitrification induces a maximum of about 23% extra ozone loss at 475K as compared to 17% in 2002–2003. Unlike these two colder Arctic winters, the 2001–2002 Arctic was warmer and did not experience much ozone loss. Sensitivity tests showed that the chosen resolution of 1° ×1° provides a better evaluation of ozone loss at the edge of the polar vortex in high solar zenith angle conditions. The simulation results for ozone, ClO, HNO3, N2O, and NOy for winters 1999–2000 and 2002–2003 were compared with measurements on board ER-2 and Geophysica aircraft respectively. Sensitivity tests showed that increasing heating rates calculated by the model by 50% and doubling the PSC (Polar Stratospheric Clouds) particle density (from 5 × 10-3 to 10-2 cm-3) refines the agreement with in situ ozone, N2O and NOy levels. In this configuration, simulated ClO levels are increased and are in better agreement with observations in January but are overestimated by about 20% in March. The use of the Burkholder et al. (1990) Cl2O2 absorption cross-sections slightly increases further ClO levels especially in high solar zenith angle conditions. Comparisons of the modelled ozone values with ozonesonde measurement in the Antarctic winter 2003 and with Polar Ozone and Aerosol Measurement III (POAM III) measurements in the Antarctic winters 2001 and 2002, shows that the simulations underestimate the ozone loss rate at the end of the ozone destruction period. A slightly better agreement is obtained with the use of Burkholder et al. (1990) Cl2O2 absorption cross-sections

Changing the mindset in life sciences toward translation: A consensus.

Participants at the recent Translate! 2014 meeting in Berlin, Germany, reached a consensus on the rate-limiting factor for advancing translational medicine

Evidence for the decay X(3872) -> J/ψω

We present a study of the decays B-0,B-+ -> J/psi pi(+)pi(-)pi K-0(0,+), using 467 x 106 B (B) over bar pairs recorded with the BABAR detector. We present evidence for the decay mode X(3872) -> J/psi omega, with product branching fractions B(B+ -> X(3872K(+)) x B(X(3872) -> J/psi omega) = [0.6 +/- 0.2(stat) +/- 0.1(syst)] x 10(-5), and B(B-0 -> X(3872)K-0) x B(X(3872) -> J/psi omega) = [0.6 +/- 0.3(stat) +/- 0.1(syst)] x 10(-5). A detailed study of the pi(+) pi(-) pi(0) mass distribution from X(3872) decay favors a negative-parity assignment