COVID-19 patients presenting with afebrile acute abdominal pain
Bibliographic record
Abstract
IMPORTANCE: With the surge in COVID-19 cases worldwide, the medical community should be aware of atypical clinical presentations to help with correct diagnosis, to take the proper measures to place the patient in isolation and to avoid healthcare professionals being infected by coronavirus (SARS-CoV-2). OBJECTIVE: To report that patients who subsequently test positive for COVID-19 may present with acute abdominal pain and no pulmonary symptoms, although they already have typical lung lesions on computed tomography (CT) scan. DESIGN, SETTING AND PARTICIPANTS: This case series is about three patients who presented to the emergency department of a community hospital in Montpellier, France, with acute abdominal pain. RESULTS: The three patients had an elevated C-reactive protein level. CT scans demonstrated no abdominal anomaly, but bilateral lung lesions at the lung bases, typical of COVID-19 lesions, were observed. COVID-19 RT-PCR tests were positive for the three patients.The patients were transferred to the COVID-19 centre for disease control at Montpellier University Hospital. As of 29 March 2020, two of those patients are still intubated in the intensive care unit (ICU) and the third was discharged home. CONCLUSION AND RELEVANCE: COVID-19 infections may present as an acute abdominal pain. In our case series, CT scan findings helped us to suspect the correct diagnosis, which was subsequently confirmed with COVID-19 RT-PCR tests.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".