D-dimer for efficacy prediction in COVID-19 patients treated with paxlovid
Bibliographic record
Abstract
BACKGROUND: Paxlovid is one of the most effective antiviral therapies for COVID-19 patients, but no studies have explored the efficacy predictors of this drug. METHODS: To investigate whether D-dimer could be used as a predictor of paxlovid response. Our study included 394 patients diagnosed with COVID-19 who were treated with paxlovid at Xiangya Hospital from Dec 5, 2022, to Jan 31, 2023. We analyzed the composite outcome and all-cause mortality and compared the clinical and demographic data of patients with normal and abnormal D-dimer levels. RESULTS: We found that 324 patients (82.2%) with D-dimer levels were regularly compared with 70 patients (17.8%). Compared with patients with normal D-dimer levels, those with elevated D-dimer levels exhibited significantly reduced albumin levels, along with elevated levels of white blood cells, platelets, neutrophils, blood urea nitrogen, and procalcitonin. Kaplan-Meier survival curves showed that patients displaying increased D-dimer levels demonstrated a significantly higher incidence of composite disease progression within 28 days (p = 0.002) and all-cause death (p < 0.001). The multivariable adjusted Cox proportional hazard regression model also achieved consistent results in composite outcome (hazard ratio [HR] 2.21, 95% confidence interval [CI], 1.21-4.02, p = 0.009) and all-cause death (HR 8.06, 95% CI 2.74-23.71, p < 0.001). CONCLUSION: Our findings suggested that the reduced efficacy of paxlovid could be predicted by elevated D-dimer levels in COVID-19 patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".