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: BCG vaccine can boost the immune system enough to prevent healthcare workers from catching coronavirus.

Abstract:
Background: As novel coronavirus disease (COVID-19) cases continue to steeply rise globally within an unprecedented short period of time, solid evidence from large randomised controlled trials is still lacking.
Currently, numerous trials testing potential treatment and preventative options are undertaken globally.
Objectives: We summarised all currently registered clinical trials examining treatment and prevention options for COVID-19 pneumonia.
Additionally, we evaluated the quality of the retrieved interventional studies.
Data sources: The ClinicalTrials.gov, the Chinese Clinical Trial Registry and the European Union Clinical Trials Register were systematically searched.
Study eligibility criteria: Registered clinical trials examining treatment and/or prevention options for COVID-19 were included.
No language, country or study design restrictions were applied.
Withdrawn, cancelled studies and trials not reporting therapeutic or preventative strategies for COVID-19 were excluded.
Participants and interventions: No restrictions in terms of participants' age and medical background or type of intervention were enforced.
Methods: The registries were searched using the term "coronavirus" or "COVID-19" from their inception until 26th March 2020.
Additional manual search of the registries was also performed.
Eligible studies were summarised and tabulated.
Interventional trials were methodologically analysed, excluding expanded access studies and trials testing Traditional Chinese Medicine.
Results: In total, 309 trials evaluating therapeutic management options, 23 studies assessing preventive strategies and 3 studies examining both were retrieved.
Interventional treatment studies were mostly randomised (n=150, 76%) and open-label (n=73, 37%) with a median number of planned inclusions of 90 (IQR 40-200).
Major categories of interventions that are currently being investigated are discussed.
Conclusion: Numerous clinical trials have been registered since the onset of the COVID-19 pandemic.
Summarised data on these trials will assist physicians and researchers to promote patient care and guide future research efforts for COVID-19 pandemic containment.
However, up to the end of March, 2020, significant information concerning reported trials was lacking.
{"verdict": "NEI", "evidence": []}