A coagulopathic conundrum of COVID-19
Bibliographic record
Abstract
We present a case of an elderly male who presented to the hospital with a worsening cough and shortness of breath. Previous outpatient COVID-19 polymerase chain reaction test was negative, and the patient’s symptoms failed to improve despite one-week course of antibiotics. He presented to the hospital a few days later with worsening symptoms and a positive COVID-19 polymerase chain reaction test at this time. Patient was febrile, tachycardic, hypertensive, and was admitted to the intensive care unit due to desaturation on room air ultimately leading to intubation. CBC with differential showed evidence of thrombocytopenia, elevated INR/D-Dimer/fibrin split products/inflammatory markers, as well as decreased fibrinogen. He was treated for COVID-19 pneumonia and given platelets/cryoprecipitate/Vit K for suspected diffuse intravascular coagulation.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".