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LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T2 73-81 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T3 344-352 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 366-374 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 572-580 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 640-648 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 661-669 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 734-737 Disease denotes MDS http://purl.obolibrary.org/obo/MONDO_0009532|http://purl.obolibrary.org/obo/MONDO_0018881
T10 973-976 Disease denotes MDS http://purl.obolibrary.org/obo/MONDO_0009532|http://purl.obolibrary.org/obo/MONDO_0018881
T12 1240-1248 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T13 1616-1624 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T14 1768-1776 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T15 2033-2041 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 353-360 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T2 649-656 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T3 872-878 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T4 953-958 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T5 1249-1256 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T6 1516-1523 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T7 1570-1571 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 1732-1739 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T9 1872-1873 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 2006-2016 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing is

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
6 73-81 Disease denotes COVID-19 MESH:C000657245
7 198-206 Disease denotes Covid-19 MESH:C000657245
8 344-352 Disease denotes COVID-19 MESH:C000657245
9 366-374 Disease denotes COVID-19 MESH:C000657245
15 572-580 Disease denotes COVID-19 MESH:C000657245
16 640-648 Disease denotes COVID-19 MESH:C000657245
17 661-669 Disease denotes COVID-19 MESH:C000657245
18 680-686 Disease denotes deaths MESH:D003643
19 734-737 Disease denotes MDS MESH:D009190
26 973-976 Disease denotes MDS MESH:D009190
27 1240-1248 Disease denotes COVID-19 MESH:C000657245
28 1358-1364 Disease denotes deaths MESH:D003643
29 1616-1624 Disease denotes COVID-19 MESH:C000657245
30 1625-1630 Disease denotes death MESH:D003643
31 1768-1776 Disease denotes COVID-19 MESH:C000657245
33 2033-2041 Disease denotes COVID-19 MESH:C000657245

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T3 0-8 Sentence denotes Abstract
T4 10-20 Sentence denotes BACKGROUND
T5 21-174 Sentence denotes The health, social and economic consequences of the COVID-19 pandemic have loomed large as every national government made decisions about how to respond.
T6 175-384 Sentence denotes The 40 Health Systems, Covid-19 (40HS.C-19) Study aimed to investigate relationships between governments’ capacity to respond (CTR), their response stringency, scope of COVID-19 testing, and COVID-19 outcomes.
T7 386-393 Sentence denotes METHODS
T8 394-707 Sentence denotes Data to April 2020 were extracted for 40 national health systems on pre-pandemic government capacity to respond (CTR) (Global Competitiveness Index), stringency measures (Oxford COVID-19 Government Response Tracker Stringency Index), approach to COVID-19 testing and COVID-19 cases and deaths (Our-World-in-Data).
T9 708-857 Sentence denotes Multidimensional scaling (MDS) and cluster analysis were applied to examine latent dimensions and visualise country similarities and dissimilarities.
T10 858-959 Sentence denotes Outcomes were tested using multivariate and one-way analyses of variances and Kruskal-Wallis H tests.
T11 961-968 Sentence denotes RESULTS
T12 969-1093 Sentence denotes The MDS model found three dimensions explaining 91% of the variance and cluster analysis identified five national groupings.
T13 1094-1257 Sentence denotes There was no association between national governments’ pre-pandemic CTR and the adoption of early stringent public health measures or approach to COVID-19 testing.
T14 1258-1365 Sentence denotes Two national clusters applied early stringency measures and reported significantly lower cumulative deaths.
T15 1366-1637 Sentence denotes The best performing national cluster (comprising Australia, South Korea, Iceland and Taiwan) adopted relatively early stringency measures but broader testing earlier than others which was associated with a change in disease trajectory and the lowest COVID-19 death rates.
T16 1638-1786 Sentence denotes Two clusters (one with high CTR and one low) both adopted late stringency measures and narrow testing and performed least well in COVID-19 outcomes.
T17 1788-1798 Sentence denotes CONCLUSION
T18 1799-1900 Sentence denotes Early stringency measures and intrinsic national capacities to deal with a pandemic are insufficient.
T19 1901-1993 Sentence denotes Extended stringency measures, important in the short-term, are not economically sustainable.
T20 1994-2042 Sentence denotes Broad-based testing is key to managing COVID-19.