New Frailty Index Approach Predicts COVID-19 Mortality Risk
Bibliographic record
Abstract
Abstract The relationships between blood biomarkers, frailty, and the risk of death of people diagnosed with COVID-19 is unclear. In the current investigation we decided to analyze the collective effect of multiple biomarkers (laboratory markers of inflammation, blood biochemistry deviations, comorbidity, demographics) on mortality in people diagnosed with COVID-19. We analyzed baseline data of one hundred fifty-five patients (age range from twenty-six to ninety-four) diagnosed with COVID-19. Thirty-seven parameters (including major morbidities) were used to derive the frailty index (FI) and calculate the risk of death as a function of FI and individual biomarkers. Discriminative ability was assessed by the area under the receiver-operating characteristic (ROC curves). The mean frailty index was 0.17 (SD = 0.10), FI of those who survived was 0.11 (SD = 0.078) and those who died was 0.22 (SD = 0.093). In a sex-adjusted model, the FI was a more powerful predictor for mortality than age. The ROC analysis showed that models involving FI as a feature have good discriminative ability for predicting COVID-19 mortality: AUC for age was 0.77, for the FI it was 0.82, and for the fully adjusted model (age + FI) it was 0.84. Thus, the systemic effect of multiple biological processes comprising aging are elucidated using the Frailty Index approach. Assessment of the frailty index at the time of admission of a patient with COVID-19 to the clinic can help to predict the high risks of severe disease and mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".