M25 Manzanillo stats & predictions

<|vq_12029|<[0]: #!/usr/bin/env python [1]: # [2]: # Copyright (c) SAS Institute Inc. [3]: # [4]: # Licensed under the Apache License, Version 2.0 (the "License"); [5]: # you may not use this file except in compliance with the License. [6]: # You may obtain a copy of the License at [7]: # [8]: # http://www.apache.org/licenses/LICENSE-2.0 [9]: # [10]: # Unless required by applicable law or agreed to in writing, software [11]: # distributed under the License is distributed on an "AS IS" BASIS, [12]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. [13]: # See the License for the specific language governing permissions and [14]: # limitations under the License. [15]: # [16]: import os [17]: import subprocess [18]: from conary import versions [19]: from conary.build import repository [20]: def getRepos(): [21]: repos = [] def _getRepoForDistro( distroName ): def _doAddRepo( repoURL ): return _addLocalRepository( repoURL ) return _addRemoteRepository( repoURL ) try: repos.append( _doAddRepo( "%s://localhost/localhost/%s" % (distroName,distroName) ) ) except Exception,e: print e def _getRepoForOS( osName ): if osName == 'MacOSX': repos.append( repository.getRepositoryFromPath('/opt/local') ) def _getRepoForPlatform(): platform = 'linux32' if platform == 'linux64': repos.append( repository.getRepositoryFromPath('/opt/conary/linux64') ) elif platform == 'linux32': repos.append( repository.getRepositoryFromPath('/opt/conary/linux32') ) elif platform == 'macosx': repos.append( repository.getRepositoryFromPath('/opt/conary/macosx') ) elif platform == 'sun4u': repos.append( repository.getRepositoryFromPath('/opt/conary/sun4u') ) elif platform == 'sun4v': repos.append( repository.getRepositoryFromPath('/opt/conary/sun4v') ) else: raise RuntimeError("Unknown platform '%s'" % (platform)) return repos def getReposAndPackages(): allRepos = getRepos() allPackages = {} packageVersions = {} seenVersions = set() allVersions = [] buildIds = {} seenBuildIds = set() def addVersion(version): versionStr = str(version) if versionStr not in seenVersions: allVersions.append(version) seenVersions.add(versionStr) nameVersionList = version.getNameVersionList() if nameVersionList: nameVersionList.sort() packageName = ':'.join(nameVersionList) packageVersions.setdefault(packageName,set()).add(version) buildId = version.buildId.hex() if buildId not in seenBuildIds: buildIds.setdefault(buildId,set()).add(version) seenBuildIds.add(buildId) def addPackage(release): releaseFullNames = release.getVersion().getFullNames() assert len(releaseFullNames) >0,'release %s has no full names' % (release,) releasePackageName = ':'.join(releaseFullNames[:-1]) releasePackageVersion = releaseFullNames[-1] packageNames.add(releasePackageName) packages.setdefault(releasePackageName,{}) packageVersions.setdefault(releasePackageName,set()).add(releasePackageVersion) versionsToAdd=release.getNewerVersions() for version in versionsToAdd: addVersion(version) packagesToCheckForConflicts.setdefault(releasePackageName,{}) packagesToCheckForConflicts.setdefault(str(version),{}) packagesToCheckForConflicts[str(version)][releasePackageName] = None packagesToCheckForConflicts.setdefault(releasePackageName,str(version)) packageNames=set() packages={} packagesToCheckForConflicts={} releases=[] total=0 return allRepos,packages def checkInstallable(allRepos,packages): installable=[] conflicts=[] total=0 return installable,pairsInConflict,total def main(): try: os.chdir(os.path.dirname(os.path.abspath(__file__))) except OSError,e: print "Error changing directory:",e try: oldCwd=os.getcwd() subprocess.check_call(['git','pull']) except subprocess.CalledProcessError,e: print "Error pulling git updates:",e allRepos,packages=getReposAndPackages() installable,pairsInConflict,total=checkInstallable(allRepos,packages) output=open('installable.txt','w') output.write('#installablen') output.close() output=open('conflicts.txt','w') output.write('#conflictsn') output.close() if __name__=='__main__': main() ***** Tag Data ***** ID: 4 description: Function `checkInstallable` performs complex checks on repositories and package dependencies. start line: 119 end line: 182 dependencies: - type: Function name: getReposAndPackages start line: 58 end line: 117 context description: This function iterates over repositories and checks their installability, identifying conflicts along with handling various edge cases. algorithmic depth: 4 algorithmic depth external: N obscurity: 5 advanced coding concepts: 4 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code: 1. **Handling Multiple Repositories**: - The code iterates through multiple repositories (`allRepos`) while checking installability. - Students must ensure efficient management of resources when dealing with potentially large datasets. 2. **Complex Dependency Resolution**: - Dependencies between packages (`packages`, `packagesToCheckForConflicts`) require careful tracking. - Conflicts need resolution without missing any dependencies or creating circular dependencies. 3. **Edge Case Handling**: - Packages without full names or versions need special handling (`assert len(releaseFullNames) >0`). - Ensuring that every unique combination of versions/build IDs is considered without redundancy. 4. **Data Structures Management**: - Efficient use of sets (`seenVersions`, `seenBuildIds`) ensures uniqueness but requires careful manipulation. - Nested dictionaries (`packagesToCheckForConflicts`) demand precise key management. 5. **Performance Optimization**: - Iterating over potentially large lists (like `allVersions`, `buildIds`) efficiently without compromising accuracy. 6. **Output Formatting**: - Writing detailed outputs (`installable.txt`, `conflicts.txt`) requires precise formatting while ensuring clarity. ### Extension: 1. **Dynamic Repository Updates**: - Handle real-time updates where new repositories can be added during processing. 2. **Advanced Conflict Resolution Strategies**: - Implement more sophisticated conflict resolution algorithms beyond simple pair checking. 3. **Dependency Graph Construction**: - Build dependency graphs dynamically as repositories are processed to visualize conflicts better. 4. **Historical Analysis**: - Track historical changes across different runs to identify trends or recurring issues. 5. **Parallel Processing**: - Utilize concurrency mechanisms specific to Python (like asyncio or multiprocessing) tailored specifically to handle independent tasks within this context. ## Exercise: ### Problem Statement: You are tasked with enhancing an existing system that checks installability across multiple repositories while identifying potential conflicts between package versions/build IDs dynamically as new data arrives during processing. Your task involves two main components: 1. Extending functionality such that new repositories can be dynamically added during runtime without restarting or halting current processing operations. 2. Implementing advanced conflict resolution strategies using dependency graphs where nodes represent packages/versions/build IDs and edges represent dependencies/conflicts. Referencing [SNIPPET], expand its functionality according to these requirements: ### Requirements: 1. **Dynamic Repository Addition**: * Implement a mechanism where new repositories can be added dynamically while existing ones are being processed. * Ensure thread safety when accessing shared resources like `allRepos`. 2. **Advanced Conflict Resolution using Dependency Graphs**: * Construct dependency graphs where nodes represent individual versions/build IDs/packages. * Edges should denote dependencies/conflicts between them. * Use graph traversal algorithms (like BFS/DFS) for detecting cycles indicating unresolved conflicts. ### Input Specifications: * Initial list of repositories (`allRepos`). * Packages data structure including dependencies/conflict information (`packages`, `packagesToCheckForConflicts`). ### Output Specifications: * Two files named `installable.txt` & `conflicts.txt`. * `installable.txt`: Contains details about successfully checked installables formatted precisely as shown in [SNIPPET]. * `conflicts.txt`: Contains detailed conflict information including involved entities formatted similarly. ### Constraints: * Assume there could be thousands of repositories/packages which necessitates efficiency both time-wise & space-wise. * New repositories might arrive unpredictably during processing; handle them seamlessly without restarting ongoing processes. ## Solution: python import threading from collections import defaultdict import networkx as nx # Placeholder functions based on assumed behavior from [SNIPPET] def getNewerVersions(): pass def getVersion(): pass def getFullNames(): pass class RepositoryManager(threading.Thread): def __init__(self): super().__init__() self.all_repositories_lock = threading.Lock() self.all_repositories = [] def add_repository(self, repo): with self.all_repositories_lock: self.all_repositories.append(repo) def check_installable_dynamic(repo_manager): installables=[] conflicts=[] total=0 while True: repo_manager.all_repositories_lock.acquire() all_repos_snapshot=repo_manager.all_repositories.copy() repo_manager.all_repositories_lock.release() if not all_repos_snapshot : break ... [REUSE LOGIC FROM SNIPPET FOR ITERATING OVER REPOSITORIES AND PACKAGES HERE] ... def create_dependency_graph(packages_to_check_for_conflict): G=nx.DiGraph() ... # Main Execution Code Block repo_manager=RepositoryManager() # Start monitoring thread/process which adds new repositories dynamically here... thread_monitoring_repo_addition=threading.Thread(target=some_function_to_monitor_new_repo_addition,args=(repo_manager,)) thread_monitoring_repo_addition.start() check_installable_thread=threading.Thread(target=check_installable_dynamic,args=(repo_manager,)) check_installable_thread.start() # Wait until both threads complete execution... thread_monitoring_repo_addition.join() check_installable_thread.join() ## Follow-up exercise: 1 Modify your implementation such that it can handle historical data analysis where it keeps track of changes across different runs (e.g., storing previous run results into a database). ## Solution: python import sqlite3 class HistoricalDataManager(): ... # Extend existing methods/classes/functions here... 1: DOI:1010-1007/s00296-022-05159-z 2: # Clinical features associated with high disease activity index score among patients with rheumatoid arthritis receiving biological DMARDs therapy at a tertiary hospital setting; evidence from Ethiopia’s largest referral hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at Tikur Anbessa Specialized Hospital rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at TAH rheumatology clinic database cohort study conducted between September 2019–August 2020 period follow-up study design was employed by extracting clinical features associated with high disease activity index score among patients receiving biological DMARDs therapy at TAH rheumatology clinic database cohort study conducted between September 2019–August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyatTAHrheumatologyclinicdatabasecohortstudyconductedbetweenSeptember 2019-August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyatTAHrheumatologyclinicdatabasecohortstudyconductedbetweenSeptember 2019-August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyatTAHrheumatologyclinicdatabasecohortstudyconductedbetweenSeptember 2019-August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyatTAHrheumatologyclinicdatabasecohortstudyconductedbetweenSeptember 2019-August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyatTAHrheumatologyclinicdatabasecohortstudyconductedbetweenSeptember 2019-August 20202020periodfollow-upsstudydesignwasemployedbyextractingclinicalfeaturesassociatedwithhighdiseaseactivityindexscoreamongpatientsreceivingbiologicalDMARDStherapyataTikurAnbesaSpecializedHospitalrheumato logicclinicdatabasecohortstudyc o n d u c t e d b e t w e e n S e p t e m b e r                                                                                                                                                                                                                                                                   S e p t e m b e r           S e p t e m b e r           S e p t e m b er           S e p t ember        N i n e t h                                                                                                           N i n et h  3: Authors: Yohannes Mengistu Assefa Amare Birhanu Gebremedhin Gebreyesus Tesfahunegn Abraha Tefera Worku Belayneh Yilma Habte Selassie Demissie Zeleke Berihun Yeshambal Tadesse Awoke Melaku Kefale Abdissa Dejene Gedefaw Tesfahun Alemayehu Hailu Zewdie Dagne Wondimagegn Amare Gelaw Belete Mesfin Yimer Gebreselassie Getachew Fekadu Jemal Berhanu Mengistie Kefale Abera Fenta Mebratu Bayisa Belihu Dessie Araya Desalegn Workineh Getachew Alula Kebede Fentahun Yohannes Mitiku Gebremeskel Birhanu Mesganaw Ayenew Abebe Teklay Ejigu Abate Tadesse Dinkineshay Woldeyes Gabrehiwot Eshetu Wondimenehu Shiferaw Belayneh Hailay Kibret Zelalem Ademe Moges Mulat Tadele Muluneh Berihun Ketema Lulseged Adane Lulseged Bekele Taye Berhanu Wubet Gezahegn Tekalign Melaku Dagne Workinehu Haftom Gizaw Tilahun Asmare Demis Bizuneh Tesfaye Addisu Kebede Beyene Agumas Tirfe Teferi Yigezu Gezahegn Belaynehu Desta Merid Weldegebreal Asmelash Ambasa Mitiku Shiferaw Zeleke Tenaw Demisse Aberash Feyissa Araya Endris Kiflework Ayalnew Motuma Goshu Desalegn Ali Mohammed Ahmed Ali Mohammed Adam Ali Mohammed Melaku Mebratu Eyasu Seifu Bayisa Nebiyu Ashenafi Kebede Abrha Alemayehu Belayneh Abdi Azage Addisu Alemayehu Gizachew Afework Seifu Adane Hailay Tadesse Andargachew Temesgen Mulunesh Alazar Weldemariam Ensermu Endris Zeleke Esubalew Fantahun Berihun Abdisa Tilahun Nebyo Getinet Mulatu Yesuf Gedamu Dechasa Muluken Ashagrie Bereket Desta Tolosa Abdela Dagnachew Belete Admasie Lemma Hiwot Shiferaw Melaku Workinehu Tigabu Siraj Elmi Mohamed Abdulkadir Awol Araya Abdiyazid Ahmed Hussien Hussein Motuma Tariku Ashenafi Amanuel Fentahun Kelkilew Derese Teklay Gizaw Tasew Eskindir Girma Girma Amare Molla Abdulahi Ibrahim Mahamed Abdulahi Ahmed Abdulkadir Abdinasir Mahamed Mohamud Hassan Mohamed Omar Hassen Mohamed Ibrahim Mohamud Moosa Hashim Mohamed Salim Ahmed Mohamed Nur Mohammed Ibrahim Hussein Ismail Mohamud Khalifa Hassan Husain Motuma Mohammed Omar Khalifa Hassan Hawo Osman Jamal Osman Idris Ahmed Jamaal Jamaal Abdelrahman Musaad Abdelgadir Magid Mohamud Ahmed Jamaal Idris Jamaal Mohamed Salem Omar Jamal Hussein Saleem Ahmed Salim Hussein Magid Musaad Abdulrahman Idris Magid Musaad Farah Ahmed Musaad Musaad Ibrahim Jamaal Musaad Farah Hussein Saleem Hamza Hussein Magid Ibrahim Ahmed Khalifa Magid Musaad Mohamed Salah Omar Husain Musaad Ibrahim Hassan Idris Farah Musaad Salim Magid Awad Jamaal Elmi Ismail Osman Elmi Abdirahman Osman Awad Elmi Jamaal Idilfarah Mohamed Jamaal Idilfarah Mohamed Ismail Osman Idilfarah Awad Elmi Idilfarah Awad Osman Abdulrahman Osman Idilfarah Awad Elmi Jamla Umer Awad Ali Yusuf Jamaal Idris Jamla Umer Jamaal Ali Yusuf Jamla Umer Awad Ali Yusuf Jamla Umer Awad Ali Yusuf Jamla Umer Awad Ali Yusuf Jamla Umer Awad Ali Yusuf Jamla Umer Abdulrahman Idris Idilfarah Abdulrahman Idris Ismail Osman Jamal Omar Hawo Osman Jamal Omar Hawo Osman Jamal Omar Hawo Osman Jamal Omar Hawo Osman Jamal Omar Hawo Osman Jamal Omar Hawo Osma nJam alOmarMohamedSal ahOmarHus ainMus adIbra h imJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAw adAliY usufJama alI drisiJam laU merAbd ulra h manId ilfa raAhmedJa ma lI dri sJa ma lA wa dAl iYu su fJa ma lI dri sJa ma lA wa dAl iYu su fJa ma lI dri sJa ma lA wa dAl iYu su fJa ma lI dri sJa ma lA wa dAl iYu su fJa ma lI dri sAbdu lr ha manId ilfa raAhmedJa ma lId ilfa raAbdu lr ha manIsma ilOsma nJa malO marHa woOsma nJa malO marHa woOsma nJa malO marHa woOsma nJa malO marHa woOsma n Ja malO marHa woOsma n Ja malO marHa woOsma n Ja malO marHa woOsma n Ja malMoh amedSa lahO marHu sainMu sa diB ra hi mA J ama liDr esiMu sa diIsma ilOsma nMo ham edMa ha medMu ha medMu ham edMu ha medAh me dMa ha medMu ham edMu ha medAh me dMu ham edMo ham edAh me dAh me dMo ham edM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedM oh amedMo ham edMo ham edMo ham edMo ham edMo ham edMo ham edMo ham edMo ham edMOhammedAbdurrah manAbdurrah manAbdurrah manAbdurrah manAbdurrah manAbdurrah manAbdurrah manAbdurrah manAbdurrah MOhammedSal ahOmarHu sainMu sa diIBra hi mM J ama liDer esiMu sa diIsma ilOsmanaMOhammedMa ha medMUha medMUha medMHame ddMUha medMAhm ee dMUha medMAhm ee dMUha medMAhm ee dMUha medMAhm ee dMUha medMAhm ee dMUha medMAhm ee dMOhammedSal ahOmarHu sainMusadiIBra hi mM J ama liDer esiMu sa diIsma ilOSmanaMOhammedMa ha mediated Mu ha mediated Mu ha mediated Mu ha mediated Mu ha mediated Mu ha mediated Mu ha mediated Mu ha mediated MUhamme ded MAhad emded MAhad emded MAhad emded MAhad emded MAhad emded MAhad emded MAhad emded MOhamme ded MOhamme ded MOhamme ded MOhamme ded MOhamme ded MOhamme ded MOhamme ded MOHAMMEDABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANNABDURRAHMANN ABDOULRHA MANIDILFA RA AHMED JAMAL IDRI SIJAMLAUMERAW ADALIU SUF JAMA ALIDRI SIJAMLAUMERAW ADALIU SUF JAMA ALIDRI SIJAMLAUMERAW ADALIU SUF JAMA ALIDRI SIJAMLAUMERAW ADALIU SUISMAIL OSMAN JAMAL OMAR HA WOO SMAN JAMAL OMAR HA WOO SMAN JAMAL OMAR HA WOO SMAN JAMAL OMAR HA WOO SMAN JAMAL OMAR HA WOO SMAN JA MALOMARMOHAMMEDSA LA HO MARHU SA IN MU SA DIIBRA HI MJ AMA LI DER ESIMUSADIISMAILOSMANOMAHAMEDMAL AHOMARHU SA IN MU SA DIIBR AHIMJA MALIDERESIMUSADIISMAILOSMANOMAHAMEDMAL AHOMARHU SA IN MU SA DIIBR AHIMJA MALIDERESIMUSADIISMAILOSMANOMAHAMEDMAL AHOMARHU SA IN MU SA DIIBR AHIMJA MALIDERESIMUSADIISMAILOSMANOMAHAMEDMAL AHOMARHU SA IN MU SA DIIBR AHIMJA MALIDERESIMUSADIISMAILOSMANOMAHAMEDMAL AHOMARHU SA IN MU SA DIIBR AHIMJA MALIDERESIMUSADIISMAILOSMAN | 4: Journal: Rheumatology International 5: Date: July172022 6: ## Abstract 7: BackgroundBiologic Disease-modifying antirheumatic drugs(Biologic DMARD)are widely used agents nowadays due to its higher efficacy comparedto conventional synthetic Disease-modifying antirheumatic drugs(CS-DMARD). However,the emergenceof drug resistanceand immunogenicity makeit challengingfor physicians todetermine which patientwould benefit mostfrom Biologic treatment.Basedonthis,Biologic treatmentshouldbe started earlierthan later inorder topreserve joint functionand improve qualityof life. 8: ObjectivesThis research aimedto determine factors affecting High Disease Activity Index(DAI) scoresamong RApatientstreatedwith biologics. 9 ResultsThis retrospective observational research included123 RA patients who were treatedwith biologics.The mean ageof participants werethirty-sixyears,and majorityofthem were female.The average durationof illnessand treatmentwere respectively fiveand three years.Forty-two percentof participants had positive Rheumatoid factor(RF).The mean DAI scoreswere thirty-two points.Accordingto multivariate analysis,factors affecting High DAI scores were RF positivity(Risk ratio(RR)=1·86),high numberof tender joints(RR=1·28),swollen joints(RR=1·46),Patient Global Assessment(PGA)(RR=1·44),and elevated CRP levels(RR=1·22). 10 ConclusionRF positivity,numberof tender/swollen joints,Patient Global Assessment,and elevated CRP levels were predictorsfor High DAI scores. 11:Cliniciansshouldconsiderthese predictorswhen decidingtostartBiologic treatmentin order topreserve joint functionand improve qualityof life. 12:Cliniciansshouldconsiderthese predictorswhen decidingtostartBiologic treatmentin order topreserve joint functionand improve qualityof life. 13:Cliniciansshouldconsiderthese predictorswhen decidingtostartBiologic treatmentin order topreserve joint functionand improve qualityof life. 14:Cliniciansshouldconsiderthese predictorswhen decidingtostartBiologic treatmentin order topreserve joint functionand improve qualityof life. 15:Cliniciansshouldconsiderthese predictorswhen decidingtostartBiologic treatmentin order topreserve joint functionand improve qualityof life. 16:Cliniciansshouldconsiderthese predictors whendecidingto startBiologic treatment inorder toppreservejointfunctionanda nd improvet hequalityoftlife. 17:Clinicians should consider these predictors when decidingto start Biologic treatment inorder topp reservejointfunctionanda nd improvet hequalityoftlife. 18:Clinicians should consider these predictors when decidingto start Biologic treatment inorder topp reservejointfunctionanda nd improvet hequalityoftlife. 19:Clinicians should consider these predictors when decidingto start Biologic treatment inorder topp reservejointfunctionanda nd improvet hequalityoftlife. 20:Clinicians should consider these predictors when decidingto start Biologic treatment inorder topp reservejointfunctionanda nd improvet hequalityoftlife. 21:Clinicians should consider these predictors when decidingto start Biologic treatment inorder topp reservejointfunctionanda nd improvet hequalityoftlife. 22:Clinicians should consider these predictors whendecidingto startBiologictreatment inordertoppreservejointfunction anda nd improvequalityoftlife. 23 Abstract Background Biological Disease-modifying antirheumatic drugs(Biological DMARD)s are widely used agents nowadays due to its higher efficacy comparedto conventional synthetic Disease-modifying antirheumatic drugs(CS-DMARD).However,the emergenceof drug resistanceand immunogenicity makeit challengingfor physicians todetermine which patientwould benefit mostfrom Biological treatment.Basedonthis,Biological treatmentshouldbe started earlierthan later inorder topreserve joint functionand improve qualityof life. 24 ObjectivesThis research aimedto determine factors affecting High Disease Activity Index(DAI)scoresamong RApatientstreatedwith biologics. 25 ResultsThis retrospective observational research included123 RApatic ipantswho were treatedwith biologics.The mean ageof participants werethirty-sixyears,and majorityofthem were female.The average durationoff illnessande ttreatmentwererespectivelyfiveanda ndthreeyears.Forty-two percent(ofparticipants had positive Rheumato idfactor(RF).The mean DAIscoreswerethirty-twopoints.Accordingtot multivariate analysis,factors affecting High DAIscores werer RFpositivity(Riskratio(RR)=1·86),high numberoftenderjoints(RR=128),swollenjoints(RR146),PatientGlobalAssessment(PGA)(RR144),andelevatedCRPlevels(RR122). 26 ConclusionRFpositivity,numberoftender/swollenjoints,PatientGlobalAssessment,and elevatedCRPlevels werepredictorsfor High DAIscores. 27 CliniciansshouldconsiderthesepredictorswhendecidingtostartBiologictreatmentinordertopreservejointfunctionanda nd improv calidadde vida. 28 Clinicianssh