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: Smoking tobacco is known to damage the lungs and airways causing a range of severe respiratory problems. The evidence clearly shows Covid-19 virus attacks the respiratory system, which explains why smokers are at greater risk.

Abstract:
Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.
Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.
Methods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.
Using Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.
Assess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.
2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.
3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.
4.
Examine the association between former smokers who are using NRT or are vaping and disease severity.
5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.
6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.
7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.
8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.
Conclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.
We will undertake several sensitivity analyses to examine the potential for bias in these associations.
{"verdict": "CONTRADICT", "evidence": ["Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19."]}