Challenges in managing febrile patients in a rural emergency room during the COVID-19 pandemic
Bibliographic record
Abstract
Dear Editor, In a recent letter, Schiller and Blau addressed challenges in clinical decision-making amidst the COVID-19 pandemic.[1] The atypical presentation of diseases such as pneumonia certainly adds to the already-difficult problems of diagnostic ambiguities in testing limited environments. This concern can be more broadly applied to all febrile diseases that may or may not be associated with respiratory diseases, especially in hospitals serving medically underserved areas. In such hospitals, there is often a lack of appropriate medical equipment or personnel necessary to properly diagnose and treat a febrile patient. During the current pandemic, it has become necessary to triage, identify and isolate all questionable febrile patients and manage them in a separate, enclosed area until they are tested negative for the coronavirus.[2] However, in a hospital which lacks capabilities, it is nearly impossible to provide quality care in a well-isolated, enclosed setting. In the case of Sungju Moogang Hospital, a 55-bed rural hospital located in Sungju, South Korea, the emergency room has experienced multiple cases of febrile patients who had to be referred to tertiary medical centres due to insufficient means of appropriate testing and management. One such adolescent patient informed us that her fever of 40°C was likely due to another flare of haemophagocytic lymphohistiocytosis, which she had been diagnosed with several years prior. The parents requested a course of immunosuppressants as had been done at a university hospital, but we could not proceed any further because she did not bring any medical certificates and had no pertinent information in our hospital records. In addition, she was a candidate for COVID-19 screening because of a recent travel history, but we did not have the rapid testing equipment at hand. We decided to refer her to a tertiary medical centre where she received the diagnosis and was later informed that she subsequently underwent testing for COVID-19 and received appropriate immunosuppressant therapy to control her symptoms. In other cases where we were able to identify a patient's source of fever as more simple causes such as enterocolitis or pyelonephritis, we provided appropriate treatment within our emergency room. Studies have found that viral respiratory infections such as the coronavirus are associated with many other diseases, many of which are immune related.[3,4] As such, it is imperative that frontline medical workers not get caught up with Bayesian thinking and properly assess all febrile patients for potentially less common aetiologies. The challenges faced by hospitals serving underserved populations are inarguably greater during this pandemic, so great precaution should be taken to avoid missed or late diagnosis for potentially more serious conditions. Financial support and sponsorship: Nil. Conflicts of interest: There are no conflicts of interest.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".