Search for Charged Higgs Bosons in e+e- Collisions at \sqrt{s} = 189 GeV

A search for pair-produced charged Higgs bosons is performed with the L3 detector at LEP using data collected at a centre-of-mass energy of 188.6 GeV, corresponding to an integrated luminosity of 176.4 pb^-1. Higgs decays into a charm and a strange quark or into a tau lepton and its associated neutrino are considered. The observed events are consistent with the expectations from Standard Model background processes. A lower limit of 65.5 GeV on the charged Higgs mass is derived at 95 % confidence level, independent of the decay branching ratio Br(H^{+/-} -> tau nu)

Measurement of Triple-Gauge-Boson Couplings of the W Boson at LEP

We report on measurements of the triple-gauge-boson couplings of the W boson in $\mathrm{e^+e^-}$ collisions with the L3 detector at LEP. W-pair, single-W and single-photon events are analysed in a data sample corresponding to a total luminosity of 76.7~pb$^{-1}$ collected at centre-of-mass energies between 161~GeV and 183~GeV. CP-conserving as well as both C- and P-conserving triple-gauge-boson couplings are determined. The results, in good agreement with the Standard-Model expectations, confirm the existence of the self coupling among the electroweak gauge bosons and constrain its structure

ICAR: endoscopic skull‐base surgery

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Search for anomalous couplings in the Higgs sector at LEP

We search for a Higgs particle with anomalous couplings in the e+e- -> H gamma, e+e- -> HZ and e+e- -> He+e- processes with the L3 detector at LEP. We explore the mass range 70GeV m_H 170GeV using 176pb^-1 of integrated luminosity at a center-of-mass energy of \sqrt{s} = 189GeV. The Higgs decays H -> bb, H -> gamma gamma and H -> Z gamma are considered in the analysis. No evidence for anomalous Higgs production is found. This is interpreted in terms of limits on the anomalous couplings d, d_B, Delta g_1^Z and Delta kappa_gamma. Limits on the Gamma(H -> gamma gamma) and Gamma(H -> Z gamma) partial widths in the explored Higgs mass range are also obtained

Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review

BACKGROUND: Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE: The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS: PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS: A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS: We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies