Educational case series of electrocardiographs during the COVID-19 pandemic and the implications for therapy
Bibliographic record
Abstract
A 37-year-old Malay woman with a history of hypertension, poorly controlled diabetes mellitus and polycystic ovarian syndrome presented with a one-week history of dry cough and rhinorrhoea.She had no family history of cardiac disease or sudden cardiac death.She was in close contact with a relative who had been recently diagnosed with coronavirus disease 2019 (COVID-19).On examination, her temperature was 37.0°C, blood pressure 132/90 mmHg, heart rate 82 beats/minute, respiratory rate 20 breaths/minute and saturation 98% on room air.Physical examination revealed dual heart sounds and clear lungs.What does her electrocardiogram (ECG) at presentation (Fig. 1a) and prior to discharge two weeks later (Fig. 1b) show?Her chest radiograph was normal and there was no cardiomegaly (Fig. 2).Laboratory investigations revealed a normal full blood count with no evidence of lymphopenia, and C-reactive protein, lactate dehydrogenase and ferritin levels were not elevated.Her creatinine level was normal, and there were no electrolyte derangements.The patient was managed with three days of oral lopinavir/ritonavir, but this medication was stopped due to gastrointestinal side effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".