Severe coronavirus disease 2019 and hypogammaglobulinemia: A prospective propensity-matched study of hemoadsorption
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) patients with hypogammaglobulinemia who are sick enough to require intensive care unit (ICU) admission exhibit high mortality rates. This study investigates whether the use of rescue hemoadsorption is associated with increased survival and/or improved clinical and biological parameters. Methods: In this prospective study, critically ill COVID-19 patients were consecutively enrolled, and serum protein electrophoresis was used to screen for hypogammaglobulinemia (defined as a gamma-globulin fraction below the 10th percentile and confirmed by a serum IgG level below 700 mg/dL). Twelve patients received hemoadsorption for immunomodulation. The same laboratory parameters were collected from 24 propensity-matched control patients who did not receive hemoadsorption. Results: There was no significant survival difference between patients treated with hemoadsorption and control patients. There was also no significant post-treatment improvement in the clinical parameters or sequential organ failure assessment (SOFA) scores for patients who received hemoadsorption compared to those who did not. Notably, 90% of all patients (32/36) died before day 28, regardless of whether they received hemoadsorption. An independent association between hypogammaglobulinemia and death within 28 days was found for all patients: Hazard ratio (HR) 0.32 (0.15, 0.66), P < 0.001. Conclusion: Hemoadsorption was not associated with increased survival in COVID-19 ICU patients with hypogammaglobulinemia. Our study highlights that a diagnosis of impaired humoral immunity could be a useful clinical predictor of high mortality and of non-response to hemoadsorption for patients with severe COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".