9 datasets found
  1. f

    Formula for converting median and interquartile range (IQR) into mean and...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Xu Han; Juan Wang; Yingnan Wu; Hao Gu; Ning Zhao; Xing Liao; Miao Jiang (2023). Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD). [Dataset]. http://doi.org/10.1371/journal.pone.0284138.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xu Han; Juan Wang; Yingnan Wu; Hao Gu; Ning Zhao; Xing Liao; Miao Jiang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

  2. Dataset related to article "Incidence and predictors of hepatocellular...

    • zenodo.org
    • data.niaid.nih.gov
    Updated Jan 19, 2024
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    D Colapietro; P Maisonneuve; E Lytvyak; U Beuers; RC Verdonk; AJ van der Meer; B van Hoek; SD Kuiken; JT Brouwer; P Muratori; Alessio Aghemo; F Carella; AP van den Berg; K Zachou; GN Dalekos; DE Di Zeo-Sánchez; M Robles; RJ Andrade; AJ Montano-Loza; FF van den Brand; CD Slooter; G Macedo; R Liberal; YS de Boer; Ana LLEO; Ana LLEO; Dutch AIH Study Group; International Autoimmune Hepatitis Group; D Colapietro; P Maisonneuve; E Lytvyak; U Beuers; RC Verdonk; AJ van der Meer; B van Hoek; SD Kuiken; JT Brouwer; P Muratori; Alessio Aghemo; F Carella; AP van den Berg; K Zachou; GN Dalekos; DE Di Zeo-Sánchez; M Robles; RJ Andrade; AJ Montano-Loza; FF van den Brand; CD Slooter; G Macedo; R Liberal; YS de Boer; Dutch AIH Study Group; International Autoimmune Hepatitis Group (2024). Dataset related to article "Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis" [Dataset]. http://doi.org/10.5281/zenodo.10532883
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    Dataset updated
    Jan 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    D Colapietro; P Maisonneuve; E Lytvyak; U Beuers; RC Verdonk; AJ van der Meer; B van Hoek; SD Kuiken; JT Brouwer; P Muratori; Alessio Aghemo; F Carella; AP van den Berg; K Zachou; GN Dalekos; DE Di Zeo-Sánchez; M Robles; RJ Andrade; AJ Montano-Loza; FF van den Brand; CD Slooter; G Macedo; R Liberal; YS de Boer; Ana LLEO; Ana LLEO; Dutch AIH Study Group; International Autoimmune Hepatitis Group; D Colapietro; P Maisonneuve; E Lytvyak; U Beuers; RC Verdonk; AJ van der Meer; B van Hoek; SD Kuiken; JT Brouwer; P Muratori; Alessio Aghemo; F Carella; AP van den Berg; K Zachou; GN Dalekos; DE Di Zeo-Sánchez; M Robles; RJ Andrade; AJ Montano-Loza; FF van den Brand; CD Slooter; G Macedo; R Liberal; YS de Boer; Dutch AIH Study Group; International Autoimmune Hepatitis Group
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

    Abstract

    Background and aims: Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

    Methods: We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

    Results: A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

    Conclusions: HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

    Impact and implications: The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.

  3. f

    Data from: S1 Dataset -

    • plos.figshare.com
    xlsx
    Updated Feb 12, 2025
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    Lukundo Siame; Gift C. Chama; Sepiso K. Masenga (2025). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0312570.s002
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    xlsxAvailable download formats
    Dataset updated
    Feb 12, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Lukundo Siame; Gift C. Chama; Sepiso K. Masenga
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

  4. f

    Data from: S1 Dataset -

    • plos.figshare.com
    bin
    Updated Aug 7, 2023
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    Winnie Kibone; Felix Bongomin; Jerom Okot; Angel Lisa Nansubuga; Lincoln Abraham Tentena; Edbert Bagasha Nuwamanya; Titus Winyi; Whitney Balirwa; Sarah Kiguli; Joseph Baruch Baluku; Anthony Makhoba; Mark Kaddumukasa (2023). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0289546.s001
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    binAvailable download formats
    Dataset updated
    Aug 7, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Winnie Kibone; Felix Bongomin; Jerom Okot; Angel Lisa Nansubuga; Lincoln Abraham Tentena; Edbert Bagasha Nuwamanya; Titus Winyi; Whitney Balirwa; Sarah Kiguli; Joseph Baruch Baluku; Anthony Makhoba; Mark Kaddumukasa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundRheumatic and musculoskeletal disorders (RMDs) are associated with cardiovascular diseases (CVDs), with hypertension being the most common. We aimed to determine the prevalence of high blood pressure (HBP), awareness, treatment, and blood pressure control among patients with RMDs seen in a Rheumatology clinic in Uganda.MethodsWe conducted a cross-sectional study at the Rheumatology Clinic of Mulago National Referral Hospital (MNRH), Kampala, Uganda. Socio-demographic, clinical characteristics and anthropometric data were collected. Multivariable logistic regression was performed using STATA 16 to determine factors associated with HBP in patients with RMDs.ResultsA total of 100 participants were enrolled. Of these, majority were female (84%, n = 84) with mean age of 52.1 (standard deviation: 13.8) years and median body mass index of 28 kg/m2 (interquartile range (IQR): 24.8 kg/m2–32.9 kg/m2). The prevalence of HBP was 61% (n = 61, 95% CI: 51.5–70.5), with the majority (77%, n = 47, 95% CI: 66.5–87.6) being aware they had HTN. The prevalence of HTN was 47% (n = 47, 37.2–56.8), and none had it under control. Factors independently associated with HBP were age 46-55years (adjusted prevalence ratio (aPR): 2.5, 95% confidence interval (CI): 1.06–5.95), 56–65 years (aPR: 2.6, 95% CI: 1.09–6.15), >65 years (aPR: 2.5, 95% CI: 1.02–6.00), obesity (aPR: 3.7, 95% CI: 1.79–7.52), overweight (aPR: 2.7, 95% CI: 1.29–5.77).ConclusionThere was a high burden of HBP among people with RMDs in Uganda with poor blood pressure control, associated with high BMI and increasing age. There is a need for further assessment of the RMD specific drivers of HBP and meticulous follow up of patients with RMDs.

  5. f

    Data from: S1 Dataset -

    • plos.figshare.com
    xls
    Updated Aug 29, 2024
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    Dumisani Mfipa; Precious L. Hajison; Felistas Mpachika-Mfipa (2024). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0291585.s001
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    xlsAvailable download formats
    Dataset updated
    Aug 29, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Dumisani Mfipa; Precious L. Hajison; Felistas Mpachika-Mfipa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundBirthweight has an impact on newborn’s future health outcomes. Maternal factors, including age, delivery mode, HIV status, gestational age, parity and obstetric complications (preeclampsia or eclampsia [PE], antepartum hemorrhage [APH] and sepsis), however, have been shown as risk factors of low birthweight (LBW) elsewhere. For data-guided interventions, we aimed to identify predictors of LBW and compare newborn birthweights between different groups of maternal factors at Rev. John Chilembwe Hospital in Phalombe district, Malawi.MethodsUsing a retrospective record review study design, we extracted data from maternity registers of 1244 women and their newborns from October, 2022 to March, 2023. Data were skewed. Median test was used to compare median birthweights. Chi-square or Fisher’s exact tests were used to compare proportions of LBW among different groups of maternal factors. Multivariable logistic regression with stepwise, forward likelihood method was performed to identify predictors of LBW.ResultsMedian birthweight was 2900.00g (interquartile range [IQR]: 2600.00g to 3200.00g). Prevalence of LBW was 16.7% (n = 208). Proportions of LBW infants were higher in women with PE, APH, including women with sepsis than controls (10 [47.6%] of 21 vs 7 [58.3%] of 12 vs 191 [15.8%] of 1211, p < .001). Lower in term and postterm than preterm (46 [5.5%] of 835 vs 2 [3.7%] of 54 vs 160 [45.1%] of 355, p < .001). The odds of LBW infants were higher in preterm than term (AOR = 13.76, 95%CI: 9.54 to 19.84, p < .001), women with PE (AOR = 3.88, 95%CI: 1.35 to 11.18, p = .012), APH, including women with sepsis (AOR = 6.25, 95%CI: 1.50 to 26.11, p = .012) than controls.ConclusionPrevalence of LBW was high. Its predictors were prematurity, PE, APH and sepsis. Interventions aimed to prevent these risk factors should be prioritized to improve birthweight outcomes.

  6. f

    Data from: S1 Dataset -

    • plos.figshare.com
    xls
    Updated Oct 12, 2023
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    Alo Edin Huka; Lemessa Oljira; Adisu Birhanu Weldesenbet; Abdulmalik Abdela Bushra; Ibsa Abdusemed Ahmed; Abera Kenay Tura; Angefa Ayele Tuluka (2023). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0283143.s001
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    xlsAvailable download formats
    Dataset updated
    Oct 12, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Alo Edin Huka; Lemessa Oljira; Adisu Birhanu Weldesenbet; Abdulmalik Abdela Bushra; Ibsa Abdusemed Ahmed; Abera Kenay Tura; Angefa Ayele Tuluka
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundAlthough the survival of preterm neonates has improved, thanks to advanced and specialized neonatal intensive care, it remains the main reason for neonatal admission, death, and risk of lifelong complication. In this study, we assessed time to death and its predictors among preterm neonates admitted to neonatal intensive care units (NICU) at public hospitals in southern Ethiopia.MethodsA hospital based retrospective cohort was conducted among preterm neonates admitted to NICU at public hospitals in west Guji and Borena zones, Oromia National Regional State, southern Ethiopia. Simple random sampling technique was used to select records of preterm neonates admitted to both major hospitals in the study area. Data on neonatal condition, obstetric information, and status at discharge were collected from admission to discharge by trained research assistant through review of their medical records. Kaplan Meir curve and Log rank test were used to estimate the survival time and compare survival curves between variables. Cox-Proportional Hazards model was used to identify significant predictors of time to death at p

  7. f

    Data from: S1 Dataset -

    • plos.figshare.com
    xlsx
    Updated Nov 11, 2024
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    Lackson Mwape; Benson M. Hamooya; Emmanuel L. Luwaya; Danny Muzata; Kaole Bwalya; Chileleko Siakabanze; Agness Mushabati; Sepiso K. Masenga (2024). S1 Dataset - [Dataset]. http://doi.org/10.1371/journal.pone.0313484.s002
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    Dataset updated
    Nov 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Lackson Mwape; Benson M. Hamooya; Emmanuel L. Luwaya; Danny Muzata; Kaole Bwalya; Chileleko Siakabanze; Agness Mushabati; Sepiso K. Masenga
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundHypertension is a risk factor for cardiovascular events. Inflammation plays an important role in the development of essential hypertension. Studies assessing the association between complete blood count-based inflammatory scores (CBCIS) and hypertension are scarce. Therefore, this study aimed to determine the relationship between CBCIS and hypertension among individuals with and without human immunodeficiency virus (HIV).MethodThis was a cross-sectional study among 344 participants at Serenje District Hospital and Serenje Urban Clinic. We used structured questionnaires to collect sociodemographic, clinical and laboratory characteristics. CBCIS included lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), derived neutrophil-lymphocyte ratio (d-NLR), and differential white blood cells. The primary outcome variable was hypertension defined as systolic and diastolic blood pressure higher than or equal to 140/90 mmHg. Logistic regression was used to estimate the association between hypertension and CBCIS in statistical package for social science (SPSS) version 22.0.ResultsThe participants had a median age of 32 years (interquartile range (IQR) 24–42) and 65.1% (n = 224) were female. The prevalence of hypertension was 10.5% (n = 36). Among those with hypertension, 55.6% (n = 20) were female and 44.4% (n = 16) were male. The CBCIS significantly associated with hypertension in people living with HIV (PLWH) was PLR (adjusted odds ratio (AOR) 0.98; 95% confidence interval (CI) 0.97–0.99, p = 0.01) while in people without HIV, AMC (AOR 15.40 95%CI 3.75–63.26), ANC (AOR 1.88 95%CI 1.05–3.36), WBC (AOR 0.52 95%CI 0.31–0.87) and PLR (AOR 0.98 95%CI 0.97–0.99) were the factors associated with hypertension. Compared to people without HIV, only WBC, ANC, NLR, and d-NLR were good predictors of hypertension among PLWH.ConclusionOur study indicates a notable HIV-status driven association between CBCIS and hypertension, suggesting the use of CBICS as potential biomarkers for hypertension risk with substantial implications for early detection and preventive measures.

  8. Comparison of the hematological profile of HC users and non-users at...

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 1, 2023
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    Solomon Gedfie; Solomon Getawa; Woldeteklehaymanot Kassahun; Kiros Terefe Gashaye; Mulugeta Melku (2023). Comparison of the hematological profile of HC users and non-users at University of Gondar Comprehensive Specialized Referral Hospital, Northwest Ethiopia, 2021 (Median, IQR, and p-value) (n = 240). [Dataset]. http://doi.org/10.1371/journal.pone.0277254.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Solomon Gedfie; Solomon Getawa; Woldeteklehaymanot Kassahun; Kiros Terefe Gashaye; Mulugeta Melku
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Ethiopia, Gondar
    Description

    Comparison of the hematological profile of HC users and non-users at University of Gondar Comprehensive Specialized Referral Hospital, Northwest Ethiopia, 2021 (Median, IQR, and p-value) (n = 240).

  9. f

    Dataset.

    • figshare.com
    • plos.figshare.com
    xlsx
    Updated Oct 22, 2024
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    Megan Null; Mark Conaway; Riley Hazard; Louisa Edwards; Kabanda Taseera; Rose Muhindo; Sam Olum; Amir Abdallah Mbonde; Christopher C. Moore (2024). Dataset. [Dataset]. http://doi.org/10.1371/journal.pgph.0003797.s004
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    xlsxAvailable download formats
    Dataset updated
    Oct 22, 2024
    Dataset provided by
    PLOS Global Public Health
    Authors
    Megan Null; Mark Conaway; Riley Hazard; Louisa Edwards; Kabanda Taseera; Rose Muhindo; Sam Olum; Amir Abdallah Mbonde; Christopher C. Moore
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Sepsis is the leading cause of global death with the highest burden found in sub-Saharan Africa (sSA). The Universal Vital Assessment (UVA) score is a validated resource-appropriate clinical tool to identify hospitalized patients in sSA who are at risk of in-hospital mortality. Whether a decrease in the UVA score over 6 hours of resuscitation from sepsis is associated with improved outcomes is unknown. We aimed to determine (1) the association between 6-hour UVA score and in-hospital mortality, and (2) if a decrease in UVA score from admission to 6 hours was associated with improved in-hospital mortality. We analyzed data from participants with severe sepsis aged ≥14 years enrolled at the Mbarara Regional Referral Hospital in Uganda from October 2014 through May 2015. Among 197 participants, the median (interquartile range) age was 34 (27–47) years, 99 (50%) were female and 116 (59%) were living with HIV. At 6 hours, of the 65 participants in the high-risk group, 28 (43%) died compared to 28 (30%) of 94 in the medium-risk group (odds ratio [OR] 0.56, 95% confidence interval [CI] 0.29,1.08, p = 0.086) and 3 (9%) of 33 in the low-risk group (OR 0.13, 95% CI 0.03, 0.42, p = 0.002). In a univariate analysis of the 85 participants who improved their UVA risk group at 6 hours, 20 (23%) died compared to 39 (36%) of 107 participants who did not improve (OR 0.54, 95% CI 0.27–1.06, p = 0.055). In the multivariable analysis, the UVA score at 6 hours (adjusted OR [aOR] 1.26, 95%CI 1.10–1.45, p

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Xu Han; Juan Wang; Yingnan Wu; Hao Gu; Ning Zhao; Xing Liao; Miao Jiang (2023). Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD). [Dataset]. http://doi.org/10.1371/journal.pone.0284138.t001

Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jun 2, 2023
Dataset provided by
PLOS ONE
Authors
Xu Han; Juan Wang; Yingnan Wu; Hao Gu; Ning Zhao; Xing Liao; Miao Jiang
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

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