Evidence map›Paper›PMID 35247133›Full record

ArticleLa Radiologia medica2022

CT evaluation of lung infiltrates in the two months preceding the Coronavirus disease 19 pandemic in Canton Ticino (Switzerland): were there suspicious cases before the official first case?

Stefania Rizzo, Carola Catanese, Carla Puligheddu, Samantha Epistolio, Giulia Ramelli, Milo Frattini, Ricardo Pereira Mestre, Navarajah Nadarajah, Ermidio Rezzonico, Francesco Magoga and 4 more

Open access · hybridAbstract read
In one paragraph

Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review.International journal of environmental research and public health · 2023
    Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Stefania RizzoImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland. Stefania.rizzo@eoc.ch.ORCID http://orcid.org/0000-0002-5151-0866
Carola CataneseImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Carla PulighedduImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Samantha EpistolioIstituto Cantonale Di Patologia (ICP), Ente Ospedaliero Cantonale (EOC), Locarno, Switzerland.
Giulia RamelliIstituto Cantonale Di Patologia (ICP), Ente Ospedaliero Cantonale (EOC), Locarno, Switzerland.
Milo FrattiniIstituto Cantonale Di Patologia (ICP), Ente Ospedaliero Cantonale (EOC), Locarno, Switzerland.
Ricardo Pereira MestreService of Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale (EOC), Bellinzona, Switzerland.
Navarajah NadarajahImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Ermidio RezzonicoImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Francesco MagogaImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Lisa MilanImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Filippo Del GrandeImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Luca GiovanellaImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Luca CerianiImaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
Ente Ospedaliero Cantonale · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe main objective of this study was to assess the presence of pulmonary infiltrates with computed tomography (CT) appearance compatible with infection by coronavirus disease 2019 (COVID-19), in Canton Ticino in the 2 months preceding the first official case. Secondary aims were to compare the classification of infiltrates in the same time frame in 2020 and 2019; to compare the number of chest CT scans in the same period; to search for pathological confirmation of the virus. MATERIALS AND

methodsChest CT scans performed between January 1 and February 24 in 2019 and 2020 were collected and classified by COVID-19 Reporting and Data System (CO-RADS). Pathological presence of the virus was searched for when appropriate material was available.

resultsThe final cohort included 881 patients. Among the CO-RADS 3 and 4 categories, 30 patients had pneumonitis of unknown etiology. Pathological specimens were available in six patients but they were negative for COVID-19.

conclusionBefore the first official case of COVID-19 infection, in Canton Ticino there were about 30 cases of pneumonitis of uncertain origin, with CT appearance compatible with infection by COVID-19, but with no confirmation of the disease. The number of chest CT scans in the first two months of 2020 was > 12% compared to 2019.

Indexed as

COVID-19HumansLungPandemicsRetrospective StudiesSwitzerlandTomography, X-Ray ComputedCanton TicinoComputed tomographyCOVID-19Pandemic

Identifiers

PMID35247133
PMCPMC8897725
OpenAlexW4220669864

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.