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PMC:7796148 / 16380-17270 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
216 783-786 Gene denotes GAD Gene:2571
217 30-38 Disease denotes COVID-19 MESH:C000657245
218 39-46 Disease denotes anxiety MESH:D001007
219 249-257 Disease denotes COVID-19 MESH:C000657245
220 258-265 Disease denotes anxiety MESH:D001007
221 302-306 Disease denotes pain MESH:D010146
222 347-354 Disease denotes fatigue MESH:D005221
223 395-403 Disease denotes COVID-19 MESH:C000657245
224 404-411 Disease denotes anxiety MESH:D001007
225 866-874 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T33 39-46 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T34 258-265 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T35 302-306 Phenotype denotes pain http://purl.obolibrary.org/obo/HP_0012531
T36 347-354 Phenotype denotes fatigue http://purl.obolibrary.org/obo/HP_0012378
T37 404-411 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T143 0-356 Sentence denotes In Section 3.1, scores of the COVID-19 anxiety variable were categorized into quintiles, and the quintiles were dummy-coded with the lowest one used as the reference category [20] Regression models were used to estimate the relationship between the COVID-19 anxiety quintiles and the PHQ-15 subscales (pain, gastrointestinal, cardiopulmonary, and fatigue).
T144 357-465 Sentence denotes Model 1 included the four dummy-coded COVID-19 anxiety variables as predictors of the four PHQ-15 subscales.
T145 466-602 Sentence denotes The regression coefficient for each dummy-coded variable is interpreted as the mean difference between each quintile and the lowest one.
T146 603-700 Sentence denotes Model 1 was also run separately with the total PHQ-15 summed scale score replacing the subscales.
T147 701-890 Sentence denotes In Model 2 the covariates (age, gender, income, pre-existing health problems, and GAD) were included as predictors, with the addition of Italian region mourning for COVID-19 losses factors.