ArticleAllergy & rhinology (Providence, R.I.)2017
Differential diagnosis and proper treatment of acute rhinosinusitis: Guidance based on historical data analysis.
Article in Allergy & rhinology (Providence, R.I.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Procalcitonin as a Biomarker in Rhinosinusitis: A Systematic Review.American journal of rhinology & allergy · 2019Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe time course of rhinovirus positive and negative rhinosinusitis has not been quantified yet, which aggravates proper selection and justification of the optimum treatment for this illness. Such quantitative information would facilitate an early and proper identification of the disease and its differentiation from acute bacterial rhinosinusitis, and could diminish harmful overuse of antibiotics, arguably driven by patients' want for attention and the treating physicians' inability to offer an adequate verbal comfort in its stead.
objectiveExtraction of the quantitative information needed to identify rhinovirus positive or negative rhinosinusitis and to allow selection of the most appropriate treatment from the published time dependence of individual clinical symptoms of the disease.
methodsScrutiny (and modeling) of temporal evolution of all noteworthy symptoms of rhinosinusitis with a simple mathematical expression that relies on two adjustable parameters per symptom (and potentially a general time offset as an extra adjustable parameter).
resultsAdverse effects of rhinosinusitis can be grouped according to the sequence of their exponential appearance and ∼2.6 times slower exponential disappearance, rhinovirus negative rhinosinusitis generally improving ∼25% faster and being ∼40% less severe. The major early local symptoms (throat soreness and scratchiness, headache) vanish with a half-life of ∼1.8 days, whereas further local symptoms take ∼1.6 times longer to disappear. At least 50-60% improvement of two prominent early symptoms, sore throat and sneezing (but not of nasal discharge, cough, and hoarseness) by day 5 of the disease implies a nonbacterial origin of rhinitis and should exclude use of antibiotics.
conclusionTemporal evolution of all rhinosinusitis symptoms is qualitatively similar, which makes the early symptom decay a good proxy for, and predictor of, the disease perspective. Knowing a symptom intensity at just three to four time points suffices for reconstructing its entire time course and total intensity or gravity. This permits an easy and early identification of rhinosinusitis, and its plausible differentiation from acute bacterial rhinosinusitis, disease treatment optimization, and corresponding clinical trials simplification and/or shortening.
Identifiers
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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.