SARS-CoV-2 infection in cystic fibrosis: A multicentre prospective study with a control group, Italy, February-July 2020
Bibliographic record
Abstract
OBJECTIVE: To describe the symptoms and clinical course of SARS-CoV-2 infection in patients with cystic fibrosis (CF). METHODS: We carried out a prospective multicentre cohort study based on 32 CF centres and 6597 patients. Centres were contacted to collect baseline and follow-up data of patients who reported symptoms suggestive of COVID-19 or who had contact with a positive/suspected case between the end of February and July 2020. Symptoms and clinical course of the infection were compared between patients who tested positive by molecular testing (cases) and those who tested negative (controls). RESULTS: Thirty patients were reported from the centres, 16 of them tested positive and 14 tested negative. No differences in symptoms and outcome of the disease were observed between groups. Fever, cough, asthenia and dyspnea were the most frequently reported symptoms. Eight cases (50%) were hospitalized but none required ICU admission. Two adults with a history of lung transplant required non-invasive ventilation, none required ICU admission and all patients fully recovered without short-term sequelae. CONCLUSIONS: The course of SARS-CoV-2 in our patients was relatively favorable. However, COVID-19 should not be considered a mild disease in CF patients, particularly for those with severely impaired respiratory function and organ transplant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".