You will be shown a claim related to the COVID-19 pandemic, 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".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Side by side comparison of two fully automated sars-cov-2 antibody assays with a focus on specificity

Abstract:
BACKGROUND: In the context of the COVID-19 pandemic, numerous new serological test systems for the detection of anti-SARS-CoV-2 antibodies rapidly have become available.
However, the clinical performance of many of these is still insufficiently described.
Therefore, we compared three commercial, CE-marked, SARS-CoV-2 antibody assays side by side.
METHODS: We included a total of 1,154 specimens from pre-COVID-19 times and 65 samples from COVID-19 patients (≥14 days after symptom onset) to evaluate the test performance of SARS-CoV-2 serological assays by Abbott, Roche, and DiaSorin.
RESULTS: All three assays presented with high specificities: 99.2% (98.6-99.7) for Abbott, 99.7% (99.2-100.0) for Roche, and 98.3% (97.3-98.9) for DiaSorin.
In contrast to the manufacturers’ specifications, sensitivities only ranged from 83.1% to 89.2%.
Although the three methods were in good agreement (Cohen’s Kappa 0.71-0.87), McNemar tests revealed significant differences between results obtained from Roche and DiaSorin.
However, at low seroprevalences, the minor differences in specificity resulted in profound discrepancies of positive predictive values at 1% seroprevalence: 52.3% (36.2-67.9), 77.6% (52.8-91.5), and 32.6% (23.6-43.1) for Abbott, Roche, and DiaSorin, respectively.
CONCLUSION: We found diagnostically relevant differences in specificities for the anti-SARS-CoV-2 antibody assays by Abbott, Roche, and DiaSorin that have a significant impact on the positive predictive values of these tests.
{"verdict": "CONTRADICT", "evidence": ["Therefore, we compared three commercial, CE-marked, SARS-CoV-2 antibody assays side by side."]}