ArticleQuality management in health care
Order of Occurrence of COVID-19 Symptoms.
Article in Quality management in health care. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
Who cites it
6 citing papers in PubMed.
- Causal networks guiding large language models: application to COVID-19.Health care management science · 2025Article
- The predictive role of symptoms in COVID-19 diagnostic models: A longitudinal insight.Epidemiology and infection · 2024Article
- Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19.Sensors (Basel, Switzerland) · 2023Article
- Mid-Regional Proadrenomedullin in COVID-19-May It Act as a Predictor of Prolonged Cardiovascular Complications?International journal of molecular sciences · 2023Review
- Graph data science and machine learning for the detection of COVID-19 infection from symptoms.PeerJ. Computer science · 2023Article
- Development and validation of a predicted nomogram for mortality of COVID-19: a multicenter retrospective cohort study of 4,711 cases in multiethnic.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
BACKGROUND AND
objectivesCOVID-19 symptoms change after onset-some show early, others later. This article examines whether the order of occurrence of symptoms can improve diagnosis of COVID-19 before test results are available.
methodsIn total, 483 individuals who completed a COVID-19 test were recruited through Listservs. Participants then completed an online survey regarding their symptoms and test results. The order of symptoms was set according to (a) whether the participant had a "history of the symptom" due to a prior condition; and (b) whether the symptom "occurred first," or prior to, other symptoms of COVID-19. Two LASSO (Least Absolute Shrinkage and Selection Operator) regression models were developed. The first model, referred to as "time-invariant," used demographics and symptoms but not the order of symptom occurrence. The second model, referred to as "time-sensitive," used the same data set but included the order of symptom occurrence.
resultsThe average cross-validated area under the receiver operating characteristic (AROC) curve for the time-invariant model was 0.784. The time-sensitive model had an AROC curve of 0.799. The difference between the 2 accuracy levels was statistically significant (α < .05).
conclusionThe order of symptom occurrence made a statistically significant, but small, improvement in the accuracy of the diagnosis of COVID-19.
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Registered trials
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.