| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T160 |
0-18 |
Sentence |
denotes |
Principal findings |
| T161 |
19-176 |
Sentence |
denotes |
We have developed and validated the eight variable 4C Mortality Score in a UK prospective cohort study of 57 824 patients admitted to hospital with covid-19. |
| T162 |
177-426 |
Sentence |
denotes |
The 4C Mortality Score uses patient demographics, clinical observations, and blood parameters that are commonly available at the time of hospital admission and can accurately characterise the population of patients at high risk of death in hospital. |
| T163 |
427-655 |
Sentence |
denotes |
The score compared favourably with other models, including best-in-class machine learning techniques, and showed consistent performance across the validation cohorts, including good clinical utility in a decision curve analysis. |
| T164 |
656-785 |
Sentence |
denotes |
Model performance compared well against other generated models, with minimal loss in discrimination despite its pragmatic nature. |
| T165 |
786-1078 |
Sentence |
denotes |
A machine learning approach showed a marginal improvement in discrimination, but at the cost of interpretability, the requirement for many more input variables, and the need for an app or website calculator that might limit use at the bedside given personal protective equipment requirements. |
| T166 |
1079-1205 |
Sentence |
denotes |
The 4C Mortality Score showed good applicability within the validation cohort and consistency across all performance measures. |
