You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: 5G networks facilitate the spread of the coronavirus.

Abstract:
BACKGROUND: Traditional methods for cardiopulmonary assessment of Coronavirus Disease 2019 (COVID-19) patients pose risks to, both, patients and examiners.
This necessitates a remote examination of such patients without sacrificing information quality.
RESEARCH QUESTION: Assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining COVID-19 patients and establish an examination protocol for telerobotic ultrasound scanning.
STUDY DESIGN AND METHODS: Twenty-three COVID-19 patients were included and divided into two groups.
Twelve were non-severe cases, and 11 were severe cases.
All patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol.
Distribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction (LVEF), ventricular area ratio, pericardial effusion, and examination-related complications were recorded.
Bilateral lung lesions were evaluated by lung ultrasound score (LUS).
RESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients.
Peripheral lung lesions were clearly evaluated.
Severe cases had significantly more diseased regions [median (interquartile range), 6.0 (2.0-11.0) vs. 1.0 (0.0-2.8)] and higher LUSs [12.0 (4.0-24.0) vs. 2.0 (0.0-4.0)] than non-severe cases (both, P < 0.05 ).
One non-severe case (8.3%, 95%CI, 1.5% to 35.4%) and three severe cases (27.3%, 95%CI, 9.7% to 56.6%) were complicated by pleural effusions.
Four severe cases (36.4%, 95%CI, 15.2% to 64.6%) were complicated by pericardial effusions (vs 0% of non-severe cases, P < 0.05).
No patients had significant examination-related complications.
INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.
By following established protocols and considering medical history, clinical manifestations, and laboratory markers, it might help to evaluate the severity of COVID-19 remotely.
{"verdict": "CONTRADICT", "evidence": ["INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients."]}