2025
Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis
Kiani I, Razeghian I, Valizadeh P, Esmaeilian Y, Jannatdoust P, Khosravi B. Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis. Journal Of The American College Of Radiology 2025 PMID: 40889566, DOI: 10.1016/j.jacr.2025.08.028.Peer-Reviewed Original ResearchTransarterial chemoembolizationHepatocellular carcinoma patientsHepatocellular carcinomaPrediction model Risk Of Bias ASsessment ToolTACE treatment responsePrediction model RiskMeta-analysisCancer-related mortalityMeta-analysis aimRisk of bias assessment toolNo significant differenceTreatment responseClinical dataBias assessment toolTreatment outcomesWeb of ScienceTreatment efficacyCochrane LibraryInclusion criteriaPatientsSignificant differenceComprehensive searchChemoembolizationPerformance of artificial intelligence modelsSystematic review
2024
Individualized prediction models in ADHD: a systematic review and meta-regression
Salazar de Pablo G, Iniesta R, Bellato A, Caye A, Dobrosavljevic M, Parlatini V, Garcia-Argibay M, Li L, Cabras A, Haider Ali M, Archer L, Meehan A, Suleiman H, Solmi M, Fusar-Poli P, Chang Z, Faraone S, Larsson H, Cortese S. Individualized prediction models in ADHD: a systematic review and meta-regression. Molecular Psychiatry 2024, 29: 3865-3873. PMID: 38783054, PMCID: PMC11609101, DOI: 10.1038/s41380-024-02606-5.Peer-Reviewed Original ResearchArea under the curvePrediction model Risk Of Bias ASsessment ToolRisk of biasClinical predictorsMeta-RegressionClinical practiceLow risk of biasStudy risk of biasSystematic reviewRisk of bias assessment toolHigh risk of biasBias assessment toolDiagnosis of ADHDTreatment of ADHDPrediction model RiskTreatment responseExternally validated modelsLow riskHigh riskImplementation researchCognitive predictorsStudy qualityADHDAssessment toolStudy riskPredictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose
Song S, Dandapani H, Estrada R, Jones N, Samuels E, Ranney M. Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose. Journal Of Addiction Medicine 2024, 18: 218-239. PMID: 38591783, PMCID: PMC11150108, DOI: 10.1097/adm.0000000000001276.Peer-Reviewed Original ResearchPersistent opioid useOpioid use disorderPrediction model Risk Of Bias ASsessment ToolRisk of biasOpioid useRisk of persistent opioid useSystematic reviewRisk of bias assessment toolOpioid overdoseRisk of opioid use disorderUse disorderOpioid-related risksBias assessment toolFull-text reviewPreferred Reporting ItemsMeta-Analysis guidelinesSubstance use disorder historyPrediction model RiskReporting ItemsElectronic databasesDiagnosis historySTUDY SELECTIONAbstract reviewPrimary outcomeData extraction
2022
Social media use and mental health during the COVID-19 pandemic in young adults: a meta-analysis of 14 cross-sectional studies
Lee Y, Jeon Y, Kang S, Shin J, Jung Y, Jung S. Social media use and mental health during the COVID-19 pandemic in young adults: a meta-analysis of 14 cross-sectional studies. BMC Public Health 2022, 22: 995. PMID: 35581597, PMCID: PMC9112239, DOI: 10.1186/s12889-022-13409-0.Peer-Reviewed Original ResearchConceptsMental health outcomesHealth outcomesOdds ratioRisk of bias assessment toolAssociated with depressive symptomsMental health needsAssociated with anxiety symptomsMeasure mental health symptomsMental health symptomsMeta-analysisBias assessment toolSymptoms of anxietyCross-sectional studyRandom-effects modelHealth needsMeta-analysis reviewQuality assessmentSocial media platformCOVID-19 quarantineMental healthHealth symptomsInter-study heterogeneityDepressive symptomsSocial media platformsAnxiety symptoms
2021
Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review
Nguyen N, Picetti D, Dulai P, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review. Journal Of Crohn's And Colitis 2021, 16: 398-413. PMID: 34492100, PMCID: PMC8919806, DOI: 10.1093/ecco-jcc/jjab155.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsInflammatory bowel diseaseBowel diseaseClinical dataHigh riskRisk predictionSystematic reviewAcute severe ulcerative colitisLongitudinal disease activitySevere ulcerative colitisAdverse clinical outcomesBias assessment toolRisk of biasAvailable clinical dataMachine learning-based prediction modelsPrediction model RiskDisease activityCohort studyUlcerative colitisClinical outcomesTreatment responseClinical applicabilityLearning-based prediction modelsExternal validationPatientsRiskAssociation Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19
Domingo P, Mur I, Mateo G, Gutierrez M, Pomar V, de Benito N, Corbacho N, Herrera S, Millan L, Muñoz J, Malouf J, Molas M, Asensi V, Horcajada J, Estrada V, Gutierrez F, Torres F, Perez-Molina J, Fortun J, Villar L, Hohenthal U, Marttila H, Vuorinen T, Nordberg M, Valtonen M, Frigault M, Mansour M, Patel N, Fernandes A, Harvey L, Foulkes A, Healy B, Shah R, Bensaci A, Woolley A, Nikiforow S, Lin N, Sagar M, Shrager H, Huckins D, Axelrod M, Pincus M, Fleisher J, Lampa J, Nowak P, Vesterbacka J, Rasmuson J, Skorup P, Janols H, Niward K, Chatzidionysiou K, Asgeirsson H, Parke Å, Blennow O, Svensson A, Aleman S, Sönnerborg A, Henter J, Horne A, Al-Beidh F, Angus D, Annane D, Arabi Y, Beane A, Berry S, Bhimani Z, Bonten M, Bradbury C, Brunkhorst F, Buxton M, Cheng A, Cove M, De Jong M, Derde L, Estcourt L, Goossens H, Gordon A, Green C, Haniffa R, Ichihara N, Lamontagne F, Lawler P, Litton E, Marshall J, McArthur C, McAuley D, McGuinness S, McVerry B, Montgommery S, Mouncey P, Murthy S, Nichol A, Parke R, Parker J, Reyes F, Rowan K, Saito H, Santos M, Seymour C, Shankar-Hari M, Turgeon A, Turner A, van Bentum-Puijk W, van de Veerdonk F, Webb S, Zarychanski R, Baillie J, Beasley R, Cooper N, Fowler R, Galea J, Hills T, King A, Morpeth S, Netea M, Ogungbenro K, Pettila V, Tong S, Uyeki T, Youngstein T, Higgins A, Lorenzi E, Berry L, Salama C, Rosas I, Ruiz-Antorán B, Muñez Rubio E, Ramos Martínez A, Campos Esteban J, Avendaño Solá C, Pizov R, Sanz Sanz J, Abad-Santos F, Bautista-Hernández A, García-Fraile L, Barrios A, Gutiérrez Liarte Á, Alonso Pérez T, Rodríguez-García S, Mejía-Abril G, Prieto J, Leon R, VEIGA V, SCHEINBERG P, FARIAS D, PRATS J, CAVALCANTI A, MACHADO F, ROSA R, BERWANGER O, AZEVEDO L, LOPES R, DOURADO L, CASTRO C, ZAMPIERI F, AVEZUM A, LISBOA T, ROJAS S, COELHO J, LEITE R, CARVALHO J, ANDRADE L, SANDES A, PINTÃO M, SANTOS S, ALMEIDA T, COSTA A, GEBARA O, FREITAS F, PACHECO E, MACHADO D, MARTIN J, CONCEIÇÃO F, SIQUEIRA S, DAMIANI L, ISHIHARA L, SCHNEIDER D, DE SOUZA D, Hermine O, Mariette X, Tharaux P, Resche Rigon M, Porcher R, Ravaud P, Azoulay E, Cadranel J, Emmerich J, Fartoukh M, Guidet B, Humbert M, Lacombe K, Mahevas M, Pene F, Pourchet-Martinez V, Schlemmer F, Tibi A, Yazdanpanah Y, Dougados M, Bureau S, Horby P, Landray M, Baillie K, Buch M, Chappell L, Day J, Faust S, Haynes R, Jaki T, Jeffery K, Juszczak E, Lim W, Mafham M, Montgomery A, Mumford A, Thwaites G, Kamarulzaman A, Syed Omar S, Ponnampalavanar S, Raja Azwa R, Wong P, Kukreja A, Ong H, Sulaiman H, Basri S, Ng R, Megat Johari B, Rajasuriar R, Chong M, Neelamegam M, Syed Mansor S, Zulhaimi N, Lee C, Altice F, Price C, Malinis M, Hasan M, Wong C, Chidambaram S, Misnan N, Mohd Thabit A, Sim B, Bidin F, Mohd Abd Rahim M, Saravanamuttu S, Tuang W, Mohamed Gani Y, Thangavelu S, Tay K, Ibrahim N, Halid L, Tan K, Mukri M, Arip M, Koh H, Syed Badaruddin S, Raja Sureja L, Chun G, TORRE-CISNEROS J, MERCHANTE N, LEON R, CARCEL S, GARRIDO J, Galun E, Soriano A, Martínez J, Castán C, Paredes R, Dalmau D, Carbonell C, Espinosa G, Castro P, Muñóz J, Almuedo A, Prieto S, Pacheco I, Ratain M, Pisano J, Strek M, Adegunsoye A, Karrison T, Jozefien D, Karel F.A. V, Elisabeth D, Cedric B, Bastiaan M, Shankar-Hari M, Vale C, Godolphin P, Fisher D, Higgins J, Spiga F, Savovic J, Tierney J, Baron G, Benbenishty J, Berry L, Broman N, Cavalcanti A, Colman R, De Buyser S, Derde L, Domingo P, Omar S, Fernandez-Cruz A, Feuth T, Garcia F, Garcia-Vicuna R, Gonzalez-Alvaro I, Gordon A, Haynes R, Hermine O, Horby P, Horick N, Kumar K, Lambrecht B, Landray M, Leal L, Lederer D, Lorenzi E, Mariette X, Merchante N, Misnan N, Mohan S, Nivens M, Oksi J, Perez-Molina J, Pizov R, Porcher R, Postma S, Rajasuriar R, Ramanan A, Ravaud P, Reid P, Rutgers A, Sancho-Lopez A, Seto T, Sivapalasingam S, Soin A, Staplin N, Stone J, Strohbehn G, Sunden-Cullberg J, Torre-Cisneros J, Tsai L, van Hoogstraten H, van Meerten T, Veiga V, Westerweel P, Murthy S, Diaz J, Marshall J, Sterne J. Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19. JAMA 2021, 326: 499-518. PMID: 34228774, PMCID: PMC8261689, DOI: 10.1001/jama.2021.11330.Peer-Reviewed Original ResearchConceptsIL-6 antagonistsUsual careCause mortalitySummary odds ratiosOdds ratioEligible trialsMechanical ventilationClinical trialsMortality riskSecondary infectionCOVID-19Primary outcome measureAbsolute mortality riskBias assessment toolRisk of biasStudy selection criteriaCochrane riskSecondary outcomesI2 statisticPlaceboOutcome measuresMAIN OUTCOMEPatientsAdditional trialsPrimary analysis
2020
Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review
Arora T, Martin M, Grimshaw A, Mansour S, Wilson FP. Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review. BMJ Open 2020, 10: e042035. PMID: 33371041, PMCID: PMC7757434, DOI: 10.1136/bmjopen-2020-042035.Peer-Reviewed Original ResearchConceptsAcute kidney injuryPrediction of outcomeSystematic reviewKidney injuryOutcomes of AKIResolution of AKIProgression of AKILong-term outcomesBias assessment toolRisk of biasMultivariable predictive modelMeta-Analyses (PRISMA) guidelinesPreferred Reporting ItemsFull-text reviewPrediction model RiskAKI careCohort studyNegative long-term outcomesThird reviewerReporting ItemsAbstract screeningComprehensive searchPatientsCommon diseaseData extractionPrediction Models for Physical, Cognitive, and Mental Health Impairments After Critical Illness: A Systematic Review and Critical Appraisal
Haines KJ, Hibbert E, McPeake J, Anderson BJ, Bienvenu OJ, Andrews A, Brummel NE, Ferrante LE, Hopkins RO, Hough CL, Jackson J, Mikkelsen ME, Leggett N, Montgomery-Yates A, Needham DM, Sevin CM, Skidmore B, Still M, van Smeden M, Collins GS, Harhay MO. Prediction Models for Physical, Cognitive, and Mental Health Impairments After Critical Illness: A Systematic Review and Critical Appraisal. Critical Care Medicine 2020, 48: 1871-1880. PMID: 33060502, PMCID: PMC7673641, DOI: 10.1097/ccm.0000000000004659.Peer-Reviewed Original ResearchConceptsCritical illnessMental health impairmentSystematic reviewHealth impairmentBias assessment toolRisk of biasStudy eligibility criteriaPrediction model RiskComposite outcomeAdult patientsTrial enrollmentCochrane LibraryPatient recoveryIndependent reviewersHigh riskEligibility criteriaOutcome measurementsStandardized outcomesAdult survivorsStudy characteristicsIllnessStudy designSystematic searchImpairmentCandidate predictors
2019
Evaluation of Efficacy and Safety of Front-Line Regimens for the Treatment of Transplant Ineligible Patients with Multiple Myeloma: A Network Meta-Analysis of Phase 2/3 Randomized Controlled Trials
Giri S, Aryal M, Yu H, Grimshaw A, Pathak R, Huntington S, Dhakal B. Evaluation of Efficacy and Safety of Front-Line Regimens for the Treatment of Transplant Ineligible Patients with Multiple Myeloma: A Network Meta-Analysis of Phase 2/3 Randomized Controlled Trials. Blood 2019, 134: 2188. DOI: 10.1182/blood-2019-130389.Peer-Reviewed Original ResearchProgression-free survivalOverall response rateHematopoietic cell transplantFront-line treatmentAdverse eventsOverall survivalMM patientsMultiple myelomaFrontline regimensClinical trialsAdvisory CommitteeCommon grade 3Cumulative ranking (SUCRA) probabilitiesFront-line regimensHigher adverse eventsPrimary efficacy outcomeTransplant-ineligible patientsPhase III RCTsNetwork Meta-AnalysisBias assessment toolRisk of biasEvaluation of efficacyIndividual patient needsEfficacious regimenEfficacious regimens
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