Chest Computed Tomography for the Diagnosis of COVID-19 in Emergency Trauma Surgery Patients Who Require Urgent Care During the Pandemic: Protocol for an Umbrella Review
Notice bibliographique
Résumé
BACKGROUND: Many health care facilities in low- and middle-income countries are inadequately resourced. COVID-19 has the potential to decimate surgical health care services unless health systems take stringent measures to protect health care workers from viral exposure and ensure the continuity of specialized care for patients. Among these measures, the timely diagnosis of COVID-19 is paramount to ensure the use of protective measures and isolation of patients to prevent transmission to health care personnel caring for patients with an unknown COVID-19 status or contact during the pandemic. Besides molecular and antibody tests, chest computed tomography (CT) has been assessed as a potential tool to aid in the screening or diagnosis of COVID-19 and could be valuable in the emergency care setting. OBJECTIVE: This paper presents the protocol for an umbrella review that aims to identify and summarize the available literature on the diagnostic accuracy of chest CT for COVID-19 in trauma surgery patients requiring urgent care. The objective is to inform future recommendations on emergency care for this category of patients. METHODS: We will conduct several searches in the L·OVE (Living Overview of Evidence) platform for COVID-19, a system that performs automated regular searches in PubMed, Embase, Cochrane Central Register of Controlled Trials, and over 30 other sources. The search results will be presented according to PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis). This review will preferentially consider systematic reviews of diagnostic test accuracy studies, as well as individual studies of such design, if not included in the systematic reviews, that assessed the sensitivity and specificity of chest CT in emergency trauma surgery patients. Critical appraisal of the included studies for risk of bias will be conducted. Data will be extracted using a standardized data extraction tool. Findings will be summarized narratively, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach will be used to grade the certainty of evidence. RESULTS: Ethics approval is not required for this systematic review, as there will be no patient involvement. The search for this systematic review commenced in October 2020, and we expect to publish the findings in early 2021. The plan for dissemination is to publish the findings in a peer-reviewed journal and present our results at conferences that engage the most pertinent stakeholders. CONCLUSIONS: During the COVID-19 pandemic, protecting health care workers from infection is essential. Up-to-date information on the efficacy of diagnostic tests for detecting COVID-19 is essential. This review will serve an important role as a thorough summary to inform evidence-based recommendations on establishing effective policy and clinical guideline recommendations. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020198267; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=198267. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25207.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,070 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,014 | 0,017 |
| Bibliométrie | 0,015 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,009 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,009 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».