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
Bibliographic record
Abstract
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.
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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 | 0.070 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".