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Enregistrement W3114209404 · doi:10.4103/cjrm.cjrm_76_20

Challenges in managing febrile patients in a rural emergency room during the COVID-19 pandemic

2020· article· en· W3114209404 sur OpenAlexvenueno aff
Hanna Moon, Jooyoung Moon

Notice bibliographique

RevueCanadian Journal of Rural Medicine · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTriagePandemicMedicineCoronavirus disease 2019 (COVID-19)Medical emergencyPresentation (obstetrics)PneumoniaEmergency departmentIntensive care medicineNursingInfectious disease (medical specialty)PathologyDiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,179
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,300
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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