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update to septic_patients, speed improvements
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108
README.md
108
README.md
@ -26,9 +26,13 @@ This R package contains functions to make **microbiological, epidemiological dat
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With `AMR` you can:
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* Calculate the resistance (and even co-resistance) of microbial isolates with the `resistance` and `susceptibility` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
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* Predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function
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* Predict antimicrobial resistance for the nextcoming years with the `resistance_predict` function
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* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
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* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
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* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them. The following 12 antibiotics will be used as key antibiotics at default:
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* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
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* Specific for Gram-positives: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
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* Specific for Gram-negatives: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
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* Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology ([WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/)), to be able to:
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* Translate antibiotic codes (like *AMOX*), official names (like *amoxicillin*) and even trade names (like *Amoxil* or *Trimox*) to an [ATC code](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like *J01CA04*) and vice versa with the `abname` function
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* Get the latest antibiotic properties like hierarchic groups and [defined daily dose](https://en.wikipedia.org/wiki/Defined_daily_dose) (DDD) with units and administration form from the WHOCC website with the `atc_property` function
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@ -219,35 +223,33 @@ mydata %>% freq(myvariable)
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Factors sort on item by default:
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```r
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septic_patients %>% freq(hospital_id)
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# Frequency table of `hospital_id`
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# Frequency table of `hospital_id`
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# Class: factor
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# Length: 2000 (of which NA: 0 = 0.0%)
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# Unique: 5
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# Unique: 4
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#
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# Item Count Percent Cum. Count Cum. Percent (Factor Level)
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# --- ----- ------ -------- ----------- ------------- ---------------
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# 1 A 233 11.7% 233 11.7% 1
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# 2 B 583 29.1% 816 40.8% 2
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# 3 C 221 11.1% 1037 51.8% 3
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# 4 D 650 32.5% 1687 84.4% 4
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# 5 E 313 15.7% 2000 100.0% 5
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# 1 A 319 16.0% 319 16.0% 1
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# 2 B 661 33.1% 980 49.0% 2
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# 3 C 256 12.8% 1236 61.8% 3
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# 4 D 764 38.2% 2000 100.0% 4
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```
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This can be changed with the `sort.count` parameter:
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```r
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septic_patients %>% freq(hospital_id, sort.count = TRUE)
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# Frequency table of `hospital_id`
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# Frequency table of `hospital_id`
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# Class: factor
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# Length: 2000 (of which NA: 0 = 0.0%)
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# Unique: 5
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# Unique: 4
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#
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# Item Count Percent Cum. Count Cum. Percent (Factor Level)
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# --- ----- ------ -------- ----------- ------------- ---------------
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# 1 D 650 32.5% 650 32.5% 4
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# 2 B 583 29.1% 1233 61.7% 2
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# 3 E 313 15.7% 1546 77.3% 5
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# 4 A 233 11.7% 1779 88.9% 1
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# 5 C 221 11.1% 2000 100.0% 3
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# 1 D 764 38.2% 764 38.2% 4
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# 2 B 661 33.1% 1425 71.2% 2
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# 3 A 319 16.0% 1744 87.2% 1
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# 4 C 256 12.8% 2000 100.0% 3
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```
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All other types, like numbers, characters and dates, sort on count by default:
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@ -256,56 +258,56 @@ septic_patients %>% freq(date)
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# Frequency table of `date`
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# Class: Date
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# Length: 2000 (of which NA: 0 = 0.0%)
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# Unique: 1662
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# Unique: 1151
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#
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# Oldest: 2 January 2001
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# Newest: 18 October 2017 (+6133)
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# Median: 6 December 2009 (~53%)
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# Oldest: 2 January 2002
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# Newest: 28 December 2017 (+5839)
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# Median: 7 Augustus 2009 (~48%)
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#
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# Item Count Percent Cum. Count Cum. Percent
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# --- ----------- ------ -------- ----------- -------------
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# 1 2008-12-24 5 0.2% 5 0.2%
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# 2 2010-12-10 4 0.2% 9 0.4%
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# 3 2011-03-03 4 0.2% 13 0.6%
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# 4 2013-06-24 4 0.2% 17 0.8%
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# 5 2017-09-01 4 0.2% 21 1.1%
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# 6 2002-09-02 3 0.2% 24 1.2%
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# 7 2003-10-14 3 0.2% 27 1.4%
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# 8 2004-06-25 3 0.2% 30 1.5%
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# 9 2004-06-27 3 0.2% 33 1.7%
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# 10 2004-10-29 3 0.2% 36 1.8%
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# 11 2005-09-27 3 0.2% 39 2.0%
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# 12 2006-08-01 3 0.2% 42 2.1%
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# 13 2006-10-10 3 0.2% 45 2.2%
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# 14 2007-11-16 3 0.2% 48 2.4%
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# 15 2008-03-09 3 0.2% 51 2.5%
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# [ reached getOption("max.print.freq") -- omitted 1647 entries, n = 1949 (97.5%) ]
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# 1 2016-05-21 10 0.5% 10 0.5%
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# 2 2004-11-15 8 0.4% 18 0.9%
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# 3 2013-07-29 8 0.4% 26 1.3%
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# 4 2017-06-12 8 0.4% 34 1.7%
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# 5 2015-11-19 7 0.4% 41 2.1%
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# 6 2005-12-22 6 0.3% 47 2.4%
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# 7 2015-10-12 6 0.3% 53 2.6%
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# 8 2002-05-16 5 0.2% 58 2.9%
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# 9 2004-02-02 5 0.2% 63 3.1%
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# 10 2004-02-18 5 0.2% 68 3.4%
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# 11 2005-08-16 5 0.2% 73 3.6%
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# 12 2005-09-01 5 0.2% 78 3.9%
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# 13 2006-06-29 5 0.2% 83 4.2%
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# 14 2007-08-10 5 0.2% 88 4.4%
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# 15 2008-08-29 5 0.2% 93 4.7%
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# [ reached getOption("max.print.freq") -- omitted 1136 entries, n = 1907 (95.3%) ]
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```
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For numeric values, some extra descriptive statistics will be calculated:
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```r
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freq(runif(n = 10, min = 1, max = 5))
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# Frequency table
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# Frequency table
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# Class: numeric
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# Length: 10 (of which NA: 0 = 0.0%)
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# Unique: 10
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#
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# Mean: 2.9
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# Std. dev.: 1.3 (CV: 0.43, MAD: 1.5)
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# Five-Num: 1.5 | 1.7 | 2.6 | 4.0 | 4.7 (IQR: 2.3, CQV: 0.4)
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#
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# Mean: 3.4
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# Std. dev.: 1.3 (CV: 0.38, MAD: 1.3)
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# Five-Num: 1.6 | 2.0 | 3.9 | 4.7 | 4.8 (IQR: 2.7, CQV: 0.4)
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# Outliers: 0
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#
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# Item Count Percent Cum. Count Cum. Percent
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# --------- ------ -------- ----------- -------------
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# 1.132033 1 10.0% 1 10.0%
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# 2.226903 1 10.0% 2 20.0%
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# 2.280779 1 10.0% 3 30.0%
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# 2.640898 1 10.0% 4 40.0%
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# 2.913462 1 10.0% 5 50.0%
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# 3.364201 1 10.0% 6 60.0%
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# 3.771975 1 10.0% 7 70.0%
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# 3.802861 1 10.0% 8 80.0%
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# 3.803547 1 10.0% 9 90.0%
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# 3.985691 1 10.0% 10 100.0%
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# Item Count Percent Cum. Count Cum. Percent
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# --- --------- ------ -------- ----------- -------------
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# 1 1.568997 1 10.0% 1 10.0%
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# 2 1.993575 1 10.0% 2 20.0%
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# 3 2.022348 1 10.0% 3 30.0%
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# 4 2.236038 1 10.0% 4 40.0%
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# 5 3.579828 1 10.0% 5 50.0%
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# 6 4.178081 1 10.0% 6 60.0%
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# 7 4.394818 1 10.0% 7 70.0%
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# 8 4.689871 1 10.0% 8 80.0%
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# 9 4.698626 1 10.0% 9 90.0%
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# 10 4.751488 1 10.0% 10 100.0%
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#
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# Warning message:
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# All observations are unique.
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@ -320,7 +322,7 @@ Datasets to work with antibiotics and bacteria properties.
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```r
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# Dataset with 2000 random blood culture isolates from anonymised
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# septic patients between 2001 and 2017 in 5 Dutch hospitals
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septic_patients # A tibble: 2,000 x 47
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septic_patients # A tibble: 2,000 x 49
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# Dataset with ATC antibiotics codes, official names, trade names
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# and DDD's (oral and parenteral)
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