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Record W3170355216 · doi:10.13075/mp.5893.01091

Ozone disinfection of community pharmacies during the COVID-19 pandemic as a possible preventive measure for infection spread

2021· article· en· W3170355216 on OpenAlexaff
Piotr Merks, Urszula Religioni, Krzysztof Bilmin, Joanna Bogusz, Grzegorz Juszczyk, Agnieszka Barańska, Robert Kuthan, Ewelina Drelich, Marta Jakubowska, Damian Świeczkowski, Artur Białoszewski, Miłosz Jaguszewski, Edwin Panford-Quainoo, Régis Vaillancourt, Dariusz Białoszewski

Bibliographic record

VenueMedycyna Pracy · 2021
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Ozone Research
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Environmental healthPharmacyTransmission (telecommunications)Public healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medicine2019-20 coronavirus outbreakWork (physics)VirologyInfectious disease (medical specialty)DiseaseTelecommunicationsFamily medicineNursingComputer science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic is currently one of the major global health and economic challenges. An efficient method for reducing the transmission of the virus is a still unmet medical need. Existing experimental data have shown that coronavirus survival is negatively impacted by ozone, high temperature, and low humidity. Therefore, it is feasible to use area ozonation in pharmacies - the front line of the healthcare system. Nevertheless, further work is needed to evaluate the effectiveness of ozone disinfection to reduce the transmission of this virus in pharmacies, hospitals, and other public environments. Med Pr. 2021;72(5):529-34.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.174
GPT teacher head0.445
Teacher spread0.271 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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