LEADER 01669nam 2200469 450 001 000007272 005 20180723101219.0 010 $a88-08-13514-4 100 $a20010206d1989----km-y0itay0103----ba 101 0 $aita 102 $aIT 200 1 $aEcologia$eindividui, popolazioni, comunità$fMichael Begon, John L. Harper, Colin R. Townsend$g[Traduzione di Alfredo Suvero] 210 $aBologna$cZanichelli$d1989 215 $aXIII, 854 p.$cill.$d27 cm. 304 $aTitolo originale: Ecology, individuals, populations and communities 606 $aEcologia 676 $a574.5$v(20. ed.)$9Biologia. Ecologia 700 $aBegon$bMichael$062753 701 1$aHarper$bJohn L.$0288939 701 1$aTownsend$bColin R.$062754 702 $aSuvero,$bAlfredo 801 0$aIT$bUniversità della Basilicata - B.I.A.$gRICA$2unimarc 912 $a000007272 996 $aEcologia$974875 997 $aUNIBAS BAS $aMONAGR BAS $aMONOGR BAS $aAGRARIA CAT $aTORRE$b20$c20010206$lBAS01$h1133 CAT $aTORRE$b20$c20010206$lBAS01$h1135 CAT $aTORRE$b20$c20010206$lBAS01$h1144 CAT $aTORRE$b20$c20010226$lBAS01$h1020 CAT $aTORRE$b20$c20010226$lBAS01$h1025 CAT $c20050601$lBAS01$h1754 CAT $abatch$b01$c20050718$lBAS01$h1049 CAT $c20050718$lBAS01$h1108 CAT $c20050718$lBAS01$h1138 CAT $c20050718$lBAS01$h1152 CAT $aATR$b20$c20180723$lBAS01$h1009 CAT $aATR$b20$c20180723$lBAS01$h1012 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2DID$BDidattica$3PTS.s2.p16.8$676503$5A76503$820010206$f04$FPrestabile Didattica LEADER 01522nam0-22004211i-450 001 990007990700403321 005 20230228130825.0 035 $a000799070 035 $aFED01000799070 035 $a(Aleph)000799070FED01 100 $a20050124d1962----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aSapere scientifico e sapere filosofico$fdi L. Geymonat, P. Filiasi Carcano, A. Guzzo$gintroduzione di A. Guzzo$gpresentazione di G. Flores D'Arcais 210 $aFirenze$cSansoni$d1962 215 $aXV, 293 p.$d24 cm 225 1 $aPubblicazioni della Facoltà di Magistero dell'Università di Padova$v3 300 $aIn appendice: La fisica e i fisici di oggi / di G. Careri ; Cenni sullo sviluppo delle concezioni cosmologiche / di L. Rosino ; Attuali orientamenti della biologia / di U. D'Ancona 676 $a501$v21$zita 702 1$aGeymonat,$bLudovico$f<1908-1991> 702 1$aGuzzo,$bAugusto 702 1$aFlores d'Arcais,$bGiuseppe 702 1$aFiliasi Carcano,$bPaolo$f<1911-1977> 702 1$aCareri,$bGiorgio 702 1$aRosino,$bLeonida 702 1$aD'Ancona,$bUmberto$f<1896-1964> 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007990700403321 952 $aFONDO ROSSI 4208$bROSSI 4329$bFARBC 952 $aXI F AA.VV.53$fDFD 952 $a5/IX I 100$bbibl.35632$fFLFBC 959 $aFARBC 959 $aDFD 959 $aFLFBC 996 $aSapere scientifico e sapere filosofico$9751924 997 $aUNINA LEADER 03435nam 2200481 450 001 9910717345203321 005 20221105202939.0 035 $a(CKB)2670000000429460 035 $a(NjHacI)992670000000429460 035 $a(OCoLC)758900782 035 $a(EXLCZ)992670000000429460 100 $a20221105d2011 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRisk prediction models for hospital readmission $ea systematic review /$fDevan Kansagara [and six others] 210 1$aWashington, DC :$cDepartment of Veterans Affairs,$d2011. 215 $a1 online resource (ii, 33 pages) $cillustrations 225 1 $aEvidence-based Synthesis Program 300 $a"Evidence-based synthesis program." 300 $a"October 2011." 320 $aIncludes bibliographical references. 330 3 $aAn increasing body of literature attempts to describe and validate hospital readmission risk prediction tools. Interest in such models has grown for two reasons. First, transitional care interventions may reduce readmissions among chronically ill adults. Readmission risk assessment could be used to help target the delivery of these resource-intensive interventions to the patients at greatest risk. Ideally, models designed for this purpose would provide clinically relevant stratification of readmission risk and give information early enough during the hospitalization to trigger a transitional care intervention, many of which involve discharge planning and begin well before hospital discharge. Second, there is interest in using readmission rates as a quality metric. Recently, the Centers for Medicare & Medicaid Services (CMS) began using readmission rate as a publicly reported metric, with plans to lower reimbursement to hospitals with excess risk-standardized readmission rates. Valid risk adjustment methods are required for calculation of risk-standardized readmission rates which could, in turn, be used for hospital comparison, public reporting, and reimbursement determinations. Models designed for these purposes should have good predictive ability; be deployable in large populations; use reliable data that can be easily obtained; and use variables that are clinically related to, and validated in, the populations in which use is intended. This systematic review was performed to synthesize the available literature on validated readmission risk prediction models, describe their performance, and assess their suitability for clinical or administrative use. 410 0$aEvidence-based synthesis program (Series) 517 $aRisk prediction models for hospital readmission 606 $aHospitals$xAdmission and discharge 607 $aUnited States$2fast 608 $aTechnical reports.$2lcgft 608 $aStatistics.$2lcgft 615 0$aHospitals$xAdmission and discharge. 676 $a362.110685 700 $aKansagara$b Devan$01351781 712 02$aUnited States.$bDepartment of Veterans Affairs.$bHealth Services Research and Development Service, 712 02$aPortland VA Medical Center.$bEvidence-based Synthesis Program Center. 712 02$aEvidence-based Synthesis Program (U.S.) 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910717345203321 996 $aRisk prediction models for hospital readmission$93275648 997 $aUNINA