The early start of hemoperfusion decreases the mortality rate among severe <scp>COVID</scp>‐19 patients: A preliminary study
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease-2019 (COVID-19)-related organ failure is partly related to a sepsis-like syndrome and extreme pro-inflammatory cytokine release, named cytokine storm. Therapeutic strategies that prevent the production of or remove the pro-inflammatory cytokines could potentially be an effective therapy in critically afflicted COVID-19 patients. METHODS: (P/F) ratio less than 200 mmHg either received a single standard therapy or a combination of standard treatment for COVID-19 combined with hemoperfusion (hemofilter, HA330 D Javfron) for 4 h, in 3 consecutive days. The length of hospital stay and mechanical ventilation, the resolution of radiologic abnormalities, and the mortality rate were defined as the primary outcomes. RESULTS: Demographic characteristics, the acute physiology, and chronic health evaluation score of both groups were similar (p > 0.05). Importantly, we noticed a significant mortality rate reduction in the perfused group compared with controls (37.1% vs. 63.6%, p = 0.02), this positive effect was stronger among those with a P/F ratio higher than 75 (mortality rate of 84.7% for P/F ratio < 75 vs. 15.4% for P/F ratio ≥ 75, p = 0.02). CONCLUSIONS: The results imply that early start of hemoperfusion could be more effective and significantly reduce the mortality rate among COVID-19 patients with critical diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".