Evaluation of the association between antiphospholipid antibodies and ICU admission outcome in critically ill COVID-19 patients in Iranian population
Bibliographic record
Abstract
Background: The role of anti-phospholipid antibodies (aPLs) in the prognosis of COVID-19 patients is controversial. In order to prove the role of this factor, the necessary measures such as early initiation of anticoagulants should be started even in the early stages of the disease and in outpatients or the use of other drugs in addition to anticoagulants. We decided to investigate the role of these antibodies in ICU admission outcomes in critically ill COVID-19 patients. Methods: The case-control study was carried out in Isfahan, Iran, from March to September 2021. One hundred nine patients in the case group were selected, including patients admitted to the ICU with a COVID-19 diagnosis. The 140 patients in the control group were selected from hospitalized and outpatients with COVID-19 with PCR + and pulmonary involvement, similar to the case group without the need for ICU hospitalization. The anti B2GP1 (IgM, IgG) and anti-cardiolipin (IgM, IgG)) were compared in two groups. Results: The frequency percentage of patients in the abnormal group of anti-phospholipid antibodies was about 10% in total. No statistically significant difference in these aPLs in continued measures was observed between the two groups of patients admitted to the ICU and those outside the ICU. Also, in the logistics regression analysis, no significant association was observed. Conclusions: Therefore, the cause of coagulation in patients admitted to the ICU is not related to these aPLs. This means that aPLs could not be a good predictor of patient admission to the ICU.
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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 | 0.009 | 0.178 |
| 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".