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  • 1
    Online Resource
    Online Resource
    Weston Medical Publishing ; 2016
    In:  Journal of Opioid Management Vol. 12, No. 5 ( 2016-09-01), p. 333-345
    In: Journal of Opioid Management, Weston Medical Publishing, Vol. 12, No. 5 ( 2016-09-01), p. 333-345
    Abstract: Objective: Characterize primary care patients prescribed opioids for chronic noncancer pain (CNCP), explore guideline-recommended opioid-monitoring practices, and investigate predictors of pain agreements.Design: Retrospective chart review.Setting: Primary care clinic at a tertiary academic medical center.Patients: Adults prescribed chronic opioids (three or more monthly prescriptions within a year) for CNCP between April 1, 2014 and April 1, 2015. Patients without CNCP served as controls.Main Outcome Measure: Patient demographics, medical diagnoses, tobacco status, provider status, documentation of guideline-recommended opioid-monitoring practices, pain agreement status, and opioid prescription. Univariate statistics were used to explore differences in patient demographics, comorbidities, and guideline-recommended opioid-monitoring practices by chronic pain and pain agreement status. Logistic regression was used to investigate predictors of agreement status.Results: The clinic had 834 (9 percent) patients on chronic opioids, with 335 on a pain agreement. Documentation of opioid-monitoring practices was lacking. Logistic regression indicated that patients were significantly more likely to be on an agreement if they were Caucasian (adjusted odds ratio [OR] 2.17 [95% CI 1.41, 3.39] ), had a baseline urine drug screen (adjusted OR 10.72 [95% CI 6.16, 19.41]), were prescribed a schedule II controlled medication (adjusted OR 11.92 [95% CI 6.93, 21.62] ), and had risk assessed to some degree (adjusted OR 3.06 [95% CI 1.90, 4.96]).Conclusions: Aside from race, most patient characteristics were not predictive of pain agreement implementation. However, controlled medication of higher schedules and the use of certain guideline-recommended practices were associated with an agreement. Studies are needed to examine whether pain agreement or guideline-adherence influence clinical outcomes.
    Type of Medium: Online Resource
    ISSN: 1551-7489 , 1551-7489
    Language: Unknown
    Publisher: Weston Medical Publishing
    Publication Date: 2016
    detail.hit.zdb_id: 2397614-7
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  • 2
    In: Journal of Instrumentation, IOP Publishing, Vol. 17, No. 03 ( 2022-03-01), p. P03014-
    Abstract: Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.
    Type of Medium: Online Resource
    ISSN: 1748-0221
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2235672-1
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