The effect of hemoperfusion on treatment outcomes in COVID-19 patients with respiratory failure: a prospective study
Bibliographic record
Abstract
Introduction: COVID-19 emerged as a global clinical threat following an outbreak in China in late 2019. Objectives: The aim of the current study was to assess the effect of hemoperfusion in COVID-19 patients with respiratory failure. Patients and Methods: In this prospective study, a total of 98 patients over the age of 18 with the positive COVID-19 polymerase chain reaction (PCR) test were investigated. The patients were divided into two groups; a control group consisting of 47 patients who did not receive hemoperfusion, and an exposed group consisting of 51 patients who met the criteria for hemoperfusion. Various parameters including complete blood cell counts, serum bilirubin, creatinine, C-reactive protein (CRP), and interleukin 6 (IL-6) levels were evaluated in all patients. Results: The results of our study revealed a statistically significant difference in intensive care unit (ICU) admission between the two groups. Hospitalization time (19.941±1.75 versus 14.615±1.39, P=0.021) and ICU time (14.98±1.30 versus 9.62±1.15; P=0.003) were significantly higher in patients who received hemoperfusion. Regarding the mortality rate, only 36.7% of the patients survived; however, there was no significant difference observed between the two groups (P=0.34). Conclusion: In conclusion, the findings of our study indicate that hemoperfusion in COVID-19 patients with respiratory failure led to a significant increase in hospital stay and ICU stay compared to those without hemoperfusion. Further research is needed to determine the optimal timing and frequency of hemoperfusion to improve treatment outcomes in COVID-19 patients with respiratory failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".