Late diagnosis of COVID-19 in a 34-year-old man in a hypercoagulable state: A case report
Bibliographic record
Abstract
Background: Late diagnosis of COVID-19 in young patients in a hypercoagulable state can cause a high mortality rate. Clinical manifestations of COVID-19 include respiratory and extrapulmonary symptoms such as a hypercoagulable state, increased transaminase enzymes, and multiple-organ failure. Case and outcomes: A 34-year-old male presented to the emergency room after 3 days of high fever, weakness, and flatulence. The patient had thrombocytopenia and elevated liver transaminase enzymes and was initially diagnosed with dengue hemorrhagic fever. He was given hydration intravenous fluids, oxygen, antipyretic, and hepatoprotector. On day 4, the patient was diagnosed with COVID-19 and received therapy to decrease the Alanine transaminase and Aspartate transaminase levels. While waiting for outsourced D dimer and prothrombin time results, the patient was given low molecular weight heparin (LMWH) on day 5. On day 13, his condition deteriorated with cephalgia and shortness of breath, but the patient's family refused intubation. The chest CT scan revealed large ground-glass opacities in both lungs. The patient was given additional medications, such as Meropenem, Dexamethasone, and Remdesivir. On day 15, the patient passed away. Discussion: Intermediate LMWH dosage seems to be associated with a lower mortality incidence than standard Deep Vein Thrombosis (DVT) prophylaxis in hospitalized COVID-19 patients. However, due to the late COVID-19 diagnosis, the patient was not given LMWH at the beginning of treatment. Conclusion: A hypercoagulable state is partly responsible for the high mortality rate of COVID-19 patients. Early detection and management of the hypercoagulable state, including the use of LMWH, can decrease the severity of COVID-19 symptoms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".