A single center cohort of 40 severe COVID-19 patients who were treated with convalescent plasma
Bibliographic record
Abstract
Background/aim: A SARS-Cov2 infection which was first arised from Wuhan in December 2019 and named as COVID-19. Still there lacks either a specific treatment or a vaccine to treat COVID-19. Convalescent plasma (CP) was previously used successfully to treat SARS-CoV-1 and MERS infections. Health authority in Turkey has published a guideline to integrate this promising option in the treatment process of patients who are prone to high risk of developing severe COVID 19. Materials and Methods: Forty consecutive patients who had received CP at our center were included in the study. Demographics, COVID-19 specific parameters, biomarkers to detect the severity of COVID-19 infection and outcome variables were collected retrospectively. The correlation between outcome variables and the independent predictors of the outcome were reported. Results: Median age of the patients was 57.5 and 72.5% were male. At least one COVID-19 PCR test was confirmed to be positive in 75% of patients. Remaining 25% had a Chest-CT which was reported to be compatible with an ongoing COVID-19. All patients (100%) were classified as having severe COVID-19 infection. Over a half of the patients harbored an oxygen saturation of less than 90 despite of a continuous 5 L/min support of O2. 82.5% of the patients had a need for mechanical ventilation and 45.5% had a need for invasive mechanical ventilation. Nine out of 10 patients who have received CP outside ICU have totally recovered from COVID-19 at a median of 9 days, and a half of the patients who needed invasive mechanical ventilation were successfully free of mechanical ventilation support and managed to recover from COVID-19. Conclusion: According to the results of this study, CP is an efficient conjunct to conventional therapy against COVID-19 with a favorable safety profile.
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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 | 0.002 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".