Abdominal Imaging Utilization during the First COVID-19 Surge and Utility of Abdominal MRI
Bibliographic record
Abstract
We sought to determine relative utilization of abdominal imaging modalities in coronavirus disease 2019 (COVID-19) patients at a single institution during the first surge and evaluate whether abdominal magnetic resonance imaging (MRI) changed diagnosis and management. 1107 COVID-19 patients who had abdominal imaging were analyzed for modality and imaging setting. Patients who underwent abdominal MRI were reviewed to determine impact on management. Of 2259 examinations, 80% were inpatient, 14% were emergency, and 6% were outpatient consisting of 55% radiograph (XR), 31% computed tomography (CT), 13% ultrasound (US), and 0.6% MRI. Among 1107 patients, abdominal MRI was performed in 12 within 100 days of positive SARS-CoV-2 PCR. Indications were unrelated to COVID-19 in 75% while MRI was performed for workup of acute liver dysfunction in 25%. In 1 of 12 patients, MRI resulted in change to management unrelated to COVID-19 diagnosis. During the first surge of COVID-19 at one institution, the most common abdominal imaging examinations were radiographs and CT followed by ultrasound with the majority being performed as inpatients. Future COVID-19 surges may place disproportionate demands on inpatient abdominal radiography and CT resources. Abdominal MRI was rarely performed and did not lead to change in diagnosis or management related to COVID-19 but needs higher patient numbers for accurate assessment of utility.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".