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Record W4388572819 · doi:10.7759/cureus.48591

Development of Asthma-Like Symptoms After COVID-19: A Cross-Sectional Study in Dubai, United Arab Emirates

2023· article· en· W4388572819 on OpenAlexaff
Syed Arshad Husain, Amirarshia Rategh, Muhammad Omar Larik, Leon D’Cruz, Jean Mary John, Bassam Mahboub

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of British ColumbiaCanadian Association of Nurses in Oncology
Fundersnot available
KeywordsMedicineAsthmaPulmonologyExhaled nitric oxideSpirometryCross-sectional studyInternal medicineOutpatient clinicRespiratory systemPediatricsPathology

Abstract

fetched live from OpenAlex

Background Coronaviruses are viral agents that commonly infect animals, but have the ability to cause respiratory illness in humans, exemplified by the ongoing novel coronavirus outbreak (COVID-19). Due to the sparse literature on the effects of COVID-19 on the respiratory system, and the possible development of persistent asthma-like symptoms after infection, this cross-sectional analysis was performed in order to compare the clinical and investigative parameters between post-COVID patients and asthmatic patients. Methods A retrospective cross-sectional study was conducted on patients with prior history of COVID-19 infection that presented to the pulmonology or respiratory outpatient clinics with asthma-like symptoms and were subsequently compared to known asthmatic patients with absent history of prior COVID-19 infection, in order to evaluate the degree of similarity between both cohorts. In this study, asthma-like symptoms were defined as: (i) cough, (ii) wheezing, (iii) chest tightness, and (iv) shortness of breath. Moreover, comparisons of investigative parameters were also performed, including (i) fractional exhaled nitric oxide (FeNO), (ii) serum immunoglobulin E (IgE), (iii) absolute eosinophil counts, and (iv) qualitative spirometry results. All statistical analyses were conducted via chi-squared testing for categorical variables, and independent t-test for continuous variables. Results In this study, there were a total of 76 patients included that conformed to the eligibility criteria, including 39 patients with post-COVID symptoms with absent history of asthma or other respiratory illnesses, and 37 patients with known asthma with absent history of prior COVID-19 infection or other respiratory illnesses. Overall, this study revealed the similarities between both cohorts with respect to the incidence of cough, chest tightness, and shortness of breath. Moreover, there were similarities between the serum IgE and spirometry results. However, there were differences within the complaint of wheeze, FeNO values, and eosinophil counts between both cohorts. The placement of post-COVID patients on bronchodilator therapy involving inhaled corticosteroids and long-acting beta-agonists revealed improvement in all follow-up patients. Conclusion In conclusion, there was considerable similarity in the complaint of asthma-like symptoms after COVID-19 infection, associated with an improvement after the use of bronchodilator therapy, indicating the potential role of anti-asthma therapy (e.g., bronchodilator therapy) in managing post-COVID asthma-like symptoms. In order to validate our conclusion, further comprehensive studies with robust methodologies and larger sample populations are encouraged.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.356
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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