The Role of Disease Severity and Demographics in the Clinical Course of COVID-19 Patients Treated With Convalescent Plasma
Bibliographic record
Abstract
Treatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at the time of convalescent plasma treatment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.
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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 | 0.003 |
| 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) | 0.001 | 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".