Download splintersilk m2q

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Staying in sync is a cinch with M2Q by Splintersilk: Convert your Mint CSV files into a Quicken ready QIF file. Create Rules to automatically modify tags, memos, descriptions and

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10th revision, German modification (ICD-10-GM). In addition, diagnoses from German outpatient care include a modifier describing the diagnostic certainty (‘assured’, ‘suspected’, ‘status post’, ‘excluded’) [14]. The study population included individuals aged ≥15 years (N = 61,533,884).Selection of prognostically relevant pre-existing conditionsThe selection of pre-existing conditions was based on previous findings on risk factors for severe course of COVID-19 disease related to chronic health conditions reported in international studies. We included chronic conditions that were associated with a markedly increased age-adjusted risk in several epidemiological studies of COVID-19 patient groups or the general population based on selective literature search. Underlying chronic conditions for which there was no clear evidence in the selected literature were not considered. A markedly increased risk was generally assumed, if the risk of the outcomes hospitalization, intensive care, and/or death was increased by the factor 2 or more in age-adjusted analysis in at least one of the identified studies. The prognostically relevant chronic conditions included for the definition of high risk groups are given in Table 1.Table 1 Chronic conditions included in the study with the corresponding ICD-10 codesFull size tableAssessment of chronic conditionsIn the population of all SHI individuals in the year 2019, occurrence of the included chronic conditions was assessed on the individual level. For the majority of diseases or disease groups this was done by using the so called M2Q-criterion. Accordingly, patients were defined as having prevalent chronic diseases, if they had a diagnosis coded with the diagnostic certainty ‘assured’ for the respective condition or disease group in at least two quarters of the year 2019. For the identification of cases per disease entity according to the M2Q-criterion, it was irrelevant if identical or different diagnoses from the list of ICD-codes per disease group were assigned in at least two quarters. For disease groups with assigned subgroups (cardiovascular diseases and other neurological diseases) the M2Q-criterion had to be met for at least one of the respective subgroups.For solid tumors, hematological tumors, stroke or post-stroke conditions, cerebrovascular precursors as well as transplantations and post-transplantation conditions the case definition was modified. For the assessment of stroke Staying in sync is a cinch with M2Q by Splintersilk: Convert your Mint CSV files into a Quicken ready QIF file. Create Rules to automatically modify tags, memos, descriptions and Staying in sync is a cinch with M2Q by Splintersilk: Convert your Mint CSV files into a Quicken ready QIF file. Create Rules to automatically modify tags, memos, descriptions and Or post-stroke conditions and cerebrovascular precursors according to the M2Q-criterion, diagnoses coded with the diagnostic certainty ‘status post’ were considered in addition to ‘assured’ diagnoses. The same approach was applied for transplantations and post-transplantation conditions. In contrast, solid tumors were included as incident diseases, if the M2Q-criterion applied for at least one of the considered ICD-three-character-codes in 2018 and/or 2019 and if there was no diagnosis coded with diagnostic certainty ‘assured’ of a solid tumor in the years 2010 to 2017. Current evidence suggests an especially increased risk associated with hematological tumors [23], even with a first occurrence of the disease in the past [5]. Hence, patients who met the M2Q-criterion at least once for one of the considered ICD-three-character-codes in the years 2015 to 2019 were also included.Classification of vulnerable populations at high risk for a severe COVID-19 courseThe continuously growing study base indicates the high relevance of age and specific chronic conditions for the prognosis of severe COVID-19 courses. Early after the onset of the pandemic, a higher age emerged as the most important predictor for the need of intensive care due to COVID-19 [27, 28]. Furthermore, epidemiological studies showed a largely consistent pattern of a markedly increased risk associated with the chronic conditions included in this study. However, only limited evidence is available on the interaction of age and chronic conditions as well as chronic conditions with each other. The majority of the reviewed epidemiological studies and meta-analyses did not consider the interaction of age and chronic conditions in the statistical models applied for the risk estimation. Regarding the interplay of several comorbidities, individual studies confirm a clinically plausible accumulation of risks from single chronic conditions [29, 30].Under the pragmatic assumption of essentially aligned additive associations of age and comorbidity related risk, a high risk of severe COVID-19 courses was postulated for the following groups and classified respectively: Risk group 1: People aged 15 to 59 years with two or more prognostically relevant chronic conditions or a hematological tumor or a specific transplantation or an immunosuppressive disease Risk group 2: People aged 60 to 79 years with

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User9908

10th revision, German modification (ICD-10-GM). In addition, diagnoses from German outpatient care include a modifier describing the diagnostic certainty (‘assured’, ‘suspected’, ‘status post’, ‘excluded’) [14]. The study population included individuals aged ≥15 years (N = 61,533,884).Selection of prognostically relevant pre-existing conditionsThe selection of pre-existing conditions was based on previous findings on risk factors for severe course of COVID-19 disease related to chronic health conditions reported in international studies. We included chronic conditions that were associated with a markedly increased age-adjusted risk in several epidemiological studies of COVID-19 patient groups or the general population based on selective literature search. Underlying chronic conditions for which there was no clear evidence in the selected literature were not considered. A markedly increased risk was generally assumed, if the risk of the outcomes hospitalization, intensive care, and/or death was increased by the factor 2 or more in age-adjusted analysis in at least one of the identified studies. The prognostically relevant chronic conditions included for the definition of high risk groups are given in Table 1.Table 1 Chronic conditions included in the study with the corresponding ICD-10 codesFull size tableAssessment of chronic conditionsIn the population of all SHI individuals in the year 2019, occurrence of the included chronic conditions was assessed on the individual level. For the majority of diseases or disease groups this was done by using the so called M2Q-criterion. Accordingly, patients were defined as having prevalent chronic diseases, if they had a diagnosis coded with the diagnostic certainty ‘assured’ for the respective condition or disease group in at least two quarters of the year 2019. For the identification of cases per disease entity according to the M2Q-criterion, it was irrelevant if identical or different diagnoses from the list of ICD-codes per disease group were assigned in at least two quarters. For disease groups with assigned subgroups (cardiovascular diseases and other neurological diseases) the M2Q-criterion had to be met for at least one of the respective subgroups.For solid tumors, hematological tumors, stroke or post-stroke conditions, cerebrovascular precursors as well as transplantations and post-transplantation conditions the case definition was modified. For the assessment of stroke

2025-04-01
User8259

Or post-stroke conditions and cerebrovascular precursors according to the M2Q-criterion, diagnoses coded with the diagnostic certainty ‘status post’ were considered in addition to ‘assured’ diagnoses. The same approach was applied for transplantations and post-transplantation conditions. In contrast, solid tumors were included as incident diseases, if the M2Q-criterion applied for at least one of the considered ICD-three-character-codes in 2018 and/or 2019 and if there was no diagnosis coded with diagnostic certainty ‘assured’ of a solid tumor in the years 2010 to 2017. Current evidence suggests an especially increased risk associated with hematological tumors [23], even with a first occurrence of the disease in the past [5]. Hence, patients who met the M2Q-criterion at least once for one of the considered ICD-three-character-codes in the years 2015 to 2019 were also included.Classification of vulnerable populations at high risk for a severe COVID-19 courseThe continuously growing study base indicates the high relevance of age and specific chronic conditions for the prognosis of severe COVID-19 courses. Early after the onset of the pandemic, a higher age emerged as the most important predictor for the need of intensive care due to COVID-19 [27, 28]. Furthermore, epidemiological studies showed a largely consistent pattern of a markedly increased risk associated with the chronic conditions included in this study. However, only limited evidence is available on the interaction of age and chronic conditions as well as chronic conditions with each other. The majority of the reviewed epidemiological studies and meta-analyses did not consider the interaction of age and chronic conditions in the statistical models applied for the risk estimation. Regarding the interplay of several comorbidities, individual studies confirm a clinically plausible accumulation of risks from single chronic conditions [29, 30].Under the pragmatic assumption of essentially aligned additive associations of age and comorbidity related risk, a high risk of severe COVID-19 courses was postulated for the following groups and classified respectively: Risk group 1: People aged 15 to 59 years with two or more prognostically relevant chronic conditions or a hematological tumor or a specific transplantation or an immunosuppressive disease Risk group 2: People aged 60 to 79 years with

2025-04-01
User3856

The download jar file contains the following class files or Java source files.1.Download jodd-petite-3.4.5.jar2.Download jodd-proxetta-3.4.4-sources.jar3.Download jodd-proxetta-3.4.4.jar4.Download jodd-proxetta-3.4.5-sources.jar5.Download jodd-proxetta-3.4.5.jar6.Download jodd-lagarto-3.4.3-sources.jar7.Download jodd-lagarto-3.4.3.jar8.Download jodd-lagarto-3.4.4-sources.jar9.Download jodd-lagarto-3.4.4.jar10.Download jodd-lagarto-3.4.5-sources.jar11.Download jodd-lagarto-3.4.5.jar12.Download jodd-lagarto-web-3.4.3-sources.jar13.Download jodd-lagarto-web-3.4.3.jar14.Download jodd-lagarto-web-3.4.4-sources.jar15.Download jodd-lagarto-web-3.4.4.jar16.Download jodd-lagarto-web-3.4.5-sources.jar17.Download jodd-lagarto-web-3.4.5.jar18.Download jodd-petite-3.4.3-sources.jar19.Download jodd-petite-3.4.3.jar20.Download jodd-petite-3.4.4-sources.jar21.Download jodd-petite-3.4.4.jar22.Download jodd-proxetta-3.4.3-sources.jar23.Download jodd-proxetta-3.4.3.jar24.Download 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2025-04-20
User1121

The download jar file contains the following class files or Java source files.1.Download twitter4j-0.3-sources.jar2.Download twitter4j-0.3.jar3.Download twitter4j-2.0.0.jar4.Download twitter4j-2.0.1-sources.jar5.Download twitter4j-1.0.3.jar6.Download twitter4j-1.0.4.jar7.Download twitter4j-1.0.5.jar8.Download twitter4j-1.0.6.jar9.Download twitter4j-async-2.2.2-sources.jar10.Download twitter4j-examples-2.2.2-sources.jar11.Download twitter4j-httpclient-support-2.1.9-sources.jar12.Download twitter4j-media-support-2.1.10-sources.jar13.Download twitter4j-media-support-2.1.10.jar14.Download twitter4j-media-support-2.1.11.jar15.Download twitter4j-media-support-3.0.4-sources.jar16.Download twitter4j-media-support-3.0.4.jar17.Download twitter4j-appengine-3.0.3-sources.jar18.Download twitter4j-appengine-3.0.4-sources.jar19.Download twitter4j-appengine-3.0.4.jar20.Download twitter4j-async-2.2.0-sources.jar21.Download twitter4j-async-2.2.3-sources.jar22.Download 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twitter4j-media-support-2.1.9-sources.jar108.Download twitter4j-media-support-2.1.9.jar109.Download twitter4j-media-support-2.2.0-sources.jar110.Download twitter4j-media-support-2.2.0.jar111.Download twitter4j-media-support-2.2.1-sources.jar112.Download twitter4j-stream-2.2.0-sources.jar113.Download twitter4j-stream-2.2.1-sources.jar114.Download twitter4j-stream-2.2.2-sources.jar115.Download twitter4j-stream-2.2.3-sources.jar116.Download twitter4j-stream-2.2.4-sources.jar117.Download twitter4j-stream-3.0.0-sources.jar118.Download twitter4j-stream-3.0.1-sources.jar119.Download twitter4j-stream-3.0.2-sources.jar120.Download twitter4j-stream-3.0.3-sources.jar121.Download twitter4j-stream-3.0.4-sources.jar122.Download twitter4j-stream-3.0.4.jar123.Download twitter4j-2.0.1.jar124.Download twitter4j-2.0.10-sources.jar125.Download twitter4j-2.0.10.jar126.Download twitter4j-2.0.2-sources.jar127.Download twitter4j-2.0.2.jar128.Download twitter4j-2.0.3-sources.jar129.Download 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twitter4j-appengine-3.0.2.jar154.Download twitter4j-appengine-3.0.3.jar155.Download twitter4j-async-2.2.0.jar156.Download twitter4j-async-2.2.1-sources.jar157.Download twitter4j-async-2.2.1.jar158.Download twitter4j-async-2.2.2.jar159.Download twitter4j-async-2.2.3.jar160.Download twitter4j-async-2.2.4.jar161.Download twitter4j-async-2.2.5-sources.jar162.Download twitter4j-async-2.2.5.jar163.Download twitter4j-async-2.2.6-sources.jar164.Download twitter4j-async-2.2.6.jar165.Download twitter4j-async-3.0.2.jar166.Download twitter4j-async-3.0.3.jar167.Download twitter4j-async-android-2.2.1.jar168.Download twitter4j-async-android-2.2.3.jar169.Download twitter4j-core-2.2.0-sources.jar170.Download twitter4j-core-2.2.0.jar171.Download twitter4j-core-2.2.1-sources.jar172.Download twitter4j-core-2.2.1.jar173.Download twitter4j-core-2.2.2-sources.jar174.Download twitter4j-core-2.2.2.jar175.Download twitter4j-core-2.2.3.jar176.Download twitter4j-core-2.2.4.jar177.Download twitter4j-core-2.2.5-sources.jar178.Download twitter4j-core-2.2.5.jar179.Download twitter4j-core-2.2.6-sources.jar180.Download twitter4j-core-2.2.6.jar181.Download twitter4j-core-3.0.0-sources.jar182.Download twitter4j-core-3.0.0.jar183.Download twitter4j-core-3.0.1-sources.jar184.Download twitter4j-core-3.0.1.jar185.Download twitter4j-core-3.0.2-sources.jar186.Download twitter4j-core-3.0.2.jar187.Download twitter4j-core-3.0.3-sources.jar188.Download twitter4j-core-3.0.3.jar189.Download twitter4j-core-android-2.2.1.jar190.Download twitter4j-core-android-2.2.2.jar191.Download twitter4j-core-android-2.2.3.jar192.Download twitter4j-core-android-2.2.4.jar193.Download twitter4j-core-android-2.2.5.jar194.Download twitter4j-core-android-2.2.6.jar195.Download twitter4j-core.jar196.Download twitter4j-examples-2.1.3.jar197.Download twitter4j-examples-2.1.4.jar198.Download twitter4j-examples-2.1.5.jar199.Download twitter4j-examples-2.1.6.jar200.Download twitter4j-examples-2.1.7.jar201.Download 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twitter4j-media-support-2.2.3.jar241.Download twitter4j-media-support-2.2.4-sources.jar242.Download twitter4j-media-support-2.2.4.jar243.Download twitter4j-media-support-2.2.5-sources.jar244.Download twitter4j-media-support-2.2.5.jar245.Download twitter4j-media-support-2.2.6-sources.jar246.Download twitter4j-media-support-2.2.6.jar247.Download twitter4j-media-support-3.0.0-sources.jar248.Download twitter4j-media-support-3.0.0.jar249.Download twitter4j-media-support-3.0.1-sources.jar250.Download twitter4j-media-support-3.0.1.jar251.Download twitter4j-media-support-3.0.2-sources.jar252.Download twitter4j-media-support-3.0.2.jar253.Download twitter4j-media-support-3.0.3-sources.jar254.Download twitter4j-media-support-3.0.3.jar255.Download twitter4j-media-support-android-2.2.1.jar256.Download twitter4j-media-support-android-2.2.3.jar257.Download twitter4j-media-support-android-2.2.5.jar258.Download twitter4j-stream-2.2.0.jar259.Download twitter4j-stream-2.2.1.jar260.Download 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2025-04-12

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