MétaCan
Menu
Back to cohort
Record W4372338135 · doi:10.1016/j.clinpr.2023.100230

Treatment of COVID-19 symptoms with over the counter (OTC) medicines used for treatment of common cold and flu

2023· review· en· W4372338135 on OpenAlexaff
Ronald Eccles, Guy Boivin, Benjamin J. Cowling, Andrew T. Pavia, Rangaraj Selvarangan

Bibliographic record

VenueClinical Infection in Practice · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory and Cough-Related Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCommon coldSore throatMedicineOver-the-counterNoseCoronavirus disease 2019 (COVID-19)Nasal congestionUpper respiratory tract infectionSymptomatic treatmentRespiratory tract infectionsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakThroatIntensive care medicineRespiratory systemInternal medicineVirologyImmunologyDiseaseInfectious disease (medical specialty)SurgeryPharmacologyMedical prescription

Abstract

fetched live from OpenAlex

Persons suffering from acute upper respiratory tract viral infections (URTI) commonly use over the counter (OTC) medicines to relieve symptoms such as fever, muscle aches, cough, runny nose, sore throat and nasal congestion. At present OTC medicines are only licensed for treatment of common cold and flu symptoms and not for treatment of the same symptoms associated with COVID-19. The innate immune response responsible for the mechanisms of the symptoms of URTI is the same for all respiratory viruses including SARS-CoV-2 and these symptoms can be relieved by treatment with the same OTC medicines as available for treatment of colds and flu. This review provides scientific information that OTC treatments for common cold and flu-like illness caused by respiratory viruses are safe and effective treatments for the same symptoms associated with COVID-19.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.275
GPT teacher head0.567
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueClinical Infection in PracticeSame topicRespiratory and Cough-Related ResearchFrench-language works237,207