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Record W2980521005 · doi:10.2147/dddt.s205820

<p>Clinical And Bacteriological Impact Of Clarithromycin In Streptococcal Pharyngitis: Findings From A Meta-Analysis Of Clinical Trials</p>

2019· review· en· W2980521005 on OpenAlexaff
Daryl J. Hoban, Jos Nauta

Bibliographic record

VenueDrug Design Development and Therapy · 2019
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPharyngitisMedicineClarithromycinMeta-analysisClinical trialInternal medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: (GABHS). A 2012 meta-review and a 2016 Cochrane systematic review reported favorably on the comparative efficacy and safety of clarithromycin in pediatric patients with URTIs and in adults with GABHS pharyngitis. In this paper, the evidence base for clarithromycin in patients with URTIs is augmented by a meta-analysis of comparative studies in GABHS pharyngitis. METHODS: A series of five outpatient trials of clarithromycin for the treatment of streptococcal pharyngitis from an internal database were subjected to meta-analysis. Active comparators comprised penicillin VK and erythromycin. RESULTS: Rates of clinical cure or improvement were very similar in all treatment assignments, but the rates of bacteriological cure were numerically higher with clarithromycin than with comparator antibiotics. Adverse events data indicated that clarithromycin was generally well tolerated in these studies, with a relatively low incidence of adverse events and few severe incidents. DISCUSSION: Though currently not advised as a first-line therapy for URTI in most guidelines, the results of the meta-analysis indicate that clarithromycin is nevertheless a valid, effective and largely well-tolerated treatment option for GABHS pharyngitis patients who cannot benefit from other agents.

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)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.017
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.497
GPT teacher head0.510
Teacher spread0.013 · 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.

Study designMeta-analysis
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

Citations11
Published2019
Admission routes1
Has abstractyes

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