2019
Management of Nonvariceal Upper Gastrointestinal Bleeding: Guideline Recommendations From the International Consensus Group.
Barkun AN, Almadi M, Kuipers EJ, Laine L, Sung J, Tse F, Leontiadis GI, Abraham NS, Calvet X, Chan FKL, Douketis J, Enns R, Gralnek IM, Jairath V, Jensen D, Lau J, Lip GYH, Loffroy R, Maluf-Filho F, Meltzer AC, Reddy N, Saltzman JR, Marshall JK, Bardou M. Management of Nonvariceal Upper Gastrointestinal Bleeding: Guideline Recommendations From the International Consensus Group. Annals Of Internal Medicine 2019, 171: 805-822. PMID: 31634917, PMCID: PMC7233308, DOI: 10.7326/m19-1795.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsUpper gastrointestinal bleedingNonvariceal upper gastrointestinal bleedingHigh-risk stigmataPPI therapyGastrointestinal bleedingEndoscopic therapyCardiovascular diseaseHigh-dose proton pump inhibitor therapyEvidence profilesProton pump inhibitor therapyGlasgow-Blatchford scoreOral PPI therapyPrevious ulcer bleedingHigh-risk patientsHours of presentationManagement of patientsSuccessful endoscopic therapyStrength of recommendationsQuality of evidenceInternational consensus recommendationsInternational multidisciplinary groupInternational Consensus GroupElectronic bibliographic databasesTC-325Blatchford scoreValidation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2019, 158: 160-167. PMID: 31562847, PMCID: PMC7004228, DOI: 10.1053/j.gastro.2019.09.009.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingHospital-based interventionsComposite endpointScoring systemRockall scoreGastrointestinal bleedingClinical riskConsecutive unselected patientsLow-risk patientsClinical scoring systemRisk-scoring systemExternal validation cohortCharacteristic curve analysisInternal validation setOutpatient managementUnselected patientsValidation cohortEmergency departmentMedical CenterGreater AUCPatientsAbstractTextCurve analysisEndpointAUCMachine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review
Shung D, Simonov M, Gentry M, Au B, Laine L. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review. Digestive Diseases And Sciences 2019, 64: 2078-2087. PMID: 31055722, DOI: 10.1007/s10620-019-05645-z.Peer-Reviewed Original ResearchConceptsClinical risk scoreUpper gastrointestinal bleedingGastrointestinal bleedingOutcomes of mortalityRisk scoreSystematic reviewOvert gastrointestinal bleedingAcute gastrointestinal bleedingPrognosis Studies toolRisk of biasFull-text studiesCurrent risk assessment toolsRisk assessment toolHospital stayHemostatic interventionRisk stratificationInclusion criteriaPrognostic performanceHigh riskIndependent reviewersConference abstractsLower riskMedian AUCPatientsMortality
1991
Determination of the optimal technique for bipolar electrocoagulation treatment An experimental evaluation of the BICAP and Gold probes
Laine L. Determination of the optimal technique for bipolar electrocoagulation treatment An experimental evaluation of the BICAP and Gold probes. Gastroenterology 1991, 100: 107-112. PMID: 1983812, DOI: 10.1016/0016-5085(91)90589-d.Peer-Reviewed Original Research