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Record W3047269946 · doi:10.24926/iip.v11i3.1532

Pharmacy-Based Assessment and Management of Herpes Labialis (Cold Sores) with Antiviral Therapy

2020· article· en· W3047269946 on OpenAlexaboutno aff
Alex J. Adams, Michael E. Klepser

Bibliographic record

VenueINNOVATIONS in pharmacy · 2020
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHerpes LabialisPharmacyPharmacistAntiviral therapyIntensive care medicineVirologyVirusHerpes simplex virusFamily medicine

Abstract

fetched live from OpenAlex

Herpes labialis, commonly known as cold sores, is an infection of the mouth and surrounding area. Antiviral therapy can be used to block viral replication, which shortens the duration of symptoms, facilitates resolution of lesions, and lessens the risk of spreading the virus. Increasing access to antivirals targeted against herpes labialis by allowing assessment and prescribing by a pharmacist may decrease time to treatment for HSV-1, and improve patient satisfaction. Experience from Canada, Australia, New Zealand and the United States demonstrate that pharmacist management of cold sores has a safe track record and may be considered by other jurisdictions.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.070
GPT teacher head0.390
Teacher spread0.319 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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