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Record W2069483193 · doi:10.1055/s-2005-925524

Escalation of Antimicrobial Resistance among<i>Streptococcus pneumoniae</i>: Implications for Therapy

2005· review· en· W2069483193 on OpenAlexaff
Joseph P. Lynch, George G. Zhanel

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2005
Typereview
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineStreptococcus pneumoniaeAntibiotic resistancePenicillinAntimicrobialAntibioticsPneumoniaIntensive care medicineDrug resistanceMicrobiologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Over the past 2 decades, antimicrobial resistance among Streptococcus pneumoniae, the most common cause of community-acquired pneumonia (CAP), has escalated dramatically worldwide. In the late 1970s, strains of pneumococci displaying resistance to penicillin were described in South Africa and Spain. By the early 1990s, penicillin-resistant clones of S. pneumoniae spread rapidly across Europe and globally. Additionally, resistance to macrolides and other antibiotic classes escalated in tandem with penicillin resistance. Six international clones (serotypes 6A, 6B, 9V, 14, 19F, 23F) were responsible for most of these resistant isolates. Currently, 20 to 30% of S. pneumoniae worldwide are multidrug resistant (MDR) (i.e., resistant to > or = 3 different classes of antibiotics). Despite the dramatic escalation in the rate of antimicrobial resistance among pneumococci worldwide, the clinical impact of antimicrobial resistance is difficult to define. Treatment failures due to antibiotic-resistant pneumococci have been reported with meningitis, otitis media, and lower respiratory tract infections, but the relation between drug resistance and treatment failures has not been convincingly established. Clinical failures often reflect factors independent of antimicrobial susceptibility of the infecting organisms. Host factors (e.g., extremes of age; underlying immunosuppressive or debilitating disease; comorbidities), or factors that affect intrinsic virulence of the organisms (e.g., capsular subtype) strongly influence prognosis. Mortality rates are higher in the presence of multilobar involvement, renal insufficiency, need for intensive care unit (ICU) care, hypoxemia, severe derangement in physiological parameters, and comorbidities. Given these confounding factors, determining the impact of antimicrobial resistance on clinical outcomes is difficult, if not impossible. Prospective, randomized trials designed to assess the clinical significance of antimicrobial resistance among pneumococci are lacking, and for logistical reasons, will never be done. Does in vitro resistance translate into clinical failures? Should changing resistance patterns modify our choice of therapy for CAP or for suspected pneumococcal pneumonia? This review discusses several facets, including mechanisms of antimicrobial resistance among specific antibiotic classes, epidemiology and spread of antimicrobial resistance determinants regionally and worldwide, risk factors for acquisition and dissemination of resistance, the impact of key international clones displaying MDR, the clinical impact of antimicrobial resistance, and strategies to limit or curtail antimicrobial resistance among this key respiratory tract pathogen.

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)0.006
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.400
Teacher spread0.343 · 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

Citations72
Published2005
Admission routes1
Has abstractyes

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