Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.1016/j.ijid.2020.06.082

http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.06.082
suck pdf from google scholar
32619766!7326449!32619766
unlimited free pdf from europmc32619766    free
PDF from PMC    free
html from PMC    free

Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32619766&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215

suck abstract from ncbi

pmid32619766      Int+J+Infect+Dis 2020 ; 98 (?): 494-500
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage #MMPMID32619766
  • De Nardo P; Gentilotti E; Mazzaferri F; Cremonini E; Hansen P; Goossens H; Tacconelli E
  • Int J Infect Dis 2020[Sep]; 98 (?): 494-500 PMID32619766show ga
  • OBJECTIVE: To use Multi-Criteria Decision Analysis (MCDA) to determine weights for eleven criteria in order to prioritize COVID-19 non-critical patients for admission to hospital in healthcare settings with limited resources. METHODS: The MCDA was applied in two main steps: specification of criteria for prioritizing COVID-19 patients (and levels within each criterion); and determination of weights for the criteria based on experts' knowledge and experience in managing COVID-19 patients, via an online survey. Criteria were selected based on available COVID-19 evidence with a focus on low- and middle-income countries (LMICs). RESULTS: The most important criteria (mean weights, summing to 100%) are: PaO(2) (16.3%); peripheral O(2) saturation (15.9%); chest X-ray (14.1%); Modified Early Warning Score-MEWS (11.4%); respiratory rate (9.5%); comorbidities (6.5%); living with vulnerable people (6.4%); body mass index (5.6%); duration of symptoms before hospital evaluation (5.4%); CRP (5.1%); and age (3.8%). CONCLUSIONS: At the beginning of a new pandemic, when evidence for disease predictors is limited or unavailable and effective national contingency plans are difficult to establish, the MCDA prioritization model could play a pivotal role in improving the response of health systems.
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Betacoronavirus/genetics/*physiology[MESH]
  • |COVID-19[MESH]
  • |Coronavirus Infections/diagnosis/*therapy/virology[MESH]
  • |Decision Support Techniques[MESH]
  • |Female[MESH]
  • |Hospital Bed Capacity/*statistics & numerical data[MESH]
  • |Hospitalization/statistics & numerical data[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Patient Admission/*statistics & numerical data[MESH]
  • |Pneumonia, Viral/diagnosis/*therapy/virology[MESH]
  • |SARS-CoV-2[MESH]


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    Linkout box