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Therapeutic implications of macrolide resistance in pneumococcal community-acquired lower respiratory tract infections

2004· review· en· W1870825306 on OpenAlexaff
Thomas J. Marrie

Bibliographic record

VenueInternational Journal of Clinical Practice · 2004
Typereview
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineTolerabilityStreptococcus pneumoniaeRespiratory tract infectionsKetolideDrug resistanceAntibiotic resistanceIntensive care medicineAntibioticsMicrobiologyImmunologyAdverse effectInternal medicineRespiratory systemBiology

Abstract

fetched live from OpenAlex

Macrolide anti-bacterials are widely used for the empirical treatment of lower respiratory tract infections (RTIs) due to their activity against Streptococcus pneumoniae and other common respiratory pathogens and good safety/tolerability profile. However, the prevalence of macrolide resistance, particularly pneumococcal macrolide resistance, is increasing all around the world. The mechanisms underlying macrolide resistance include efflux pump, methylase activity and, less commonly, ribosomal mutation, which produce differing levels of resistance. Growth in macrolide resistance has been linked to the increased use of these agents, and several risk factors for the development of resistance have been identified. There are emerging data to suggest that in vitro macrolide resistance may increase the likelihood of treatment failure in patients with lower RTIs. However, at present, treatment failure is rare and randomised; intervention-based trials investigating the impact of anti-bacterial resistance on clinical outcomes are lacking. Strategies to promote appropriate use of macrolides and other anti-bacterials are needed, both to maximise therapeutic impact and to minimise the development of resistance. Furthermore, there is a need for alternative anti-bacterial agents which have high efficacy against respiratory pathogens (including resistant strains) and a low potential to induce resistance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.183
GPT teacher head0.529
Teacher spread0.347 · 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 teacher head, not a consensus.

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

Citations5
Published2004
Admission routes1
Has abstractyes

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