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Record W4399102732 · doi:10.1093/cid/ciae289

Does Adjunctive Clindamycin Have a Role in <i>Staphylococcus aureus</i> Bacteremia? A Protocol for the Adjunctive Treatment Domain of the <i>Staphylococcus aureus</i> Network Adaptive Platform (SNAP) Randomized Controlled Trial

2024· article· en· W4399102732 on OpenAlexafffund
Keerthi Anpalagan, Ravindra Dotel, Derek R. MacFadden, Simon Smith, Lesley Voss, Neta Petersiel, Michael Marks, Julie Marsh, Robert K. Mahar, Anna McGlothlin, Todd C. Lee, Anna L. Goodman, Susan C. Morpeth, Joshua S. Davis, Steven Y. C. Tong, Keerthi Anpalagan, Ravindra Dotel, Michael J. Marks, Asha C Bowen, Marc J. M. Bonten, Nick Daneman, Sebastiaan J. van Hal, George Heriot, Roger Lewis, David Chien Lye, Zoe McQuilten, David L. Paterson, James O. Robinson, Jason A. Roberts, Matthew Scarborough, Steve Webb, Lynda Whiteway, Genevieve Walls, Dafna Yahav, M.P.M. Hensgens, Matthew P. Cheng

Bibliographic record

VenueClinical Infectious Diseases · 2024
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsMcGill UniversityOttawa HospitalInstitute of Infection and ImmunityUniversity of Ottawa
FundersNational Institute of Allergy and Infectious DiseasesInstitute of Infection and ImmunityNational Health and Medical Research CouncilDepartment of Health and Social CareMedical Research Council CanadaMedical Research CouncilCanadian Institutes of Health ResearchLee Kong Chian School of Medicine, Nanyang Technological UniversityMurdoch UniversityMetro North Hospital and Health ServiceMcGill University Health CentreNational Institutes of HealthNational Institute for Health and Care ResearchStarship FoundationFiona Stanley HospitalUniversité de MontpellierHealth Research Council of New ZealandZonMwUniversitair Medisch Centrum UtrechtNational Medical Research CouncilUniversity of TorontoUniversiteit UtrechtMcGill UniversityUniversity of OxfordMonash University
KeywordsMedicineAdjunctive treatmentClindamycinAntibioticsRandomized controlled trialStaphylococcus aureusInternal medicineMicrobiologyBiologyBacteria

Abstract

fetched live from OpenAlex

BACKGROUND: The use of adjunctive antibiotics directed against exotoxin production in Staphylococcus aureus bacteremia (SAB) is widespread, and it is recommended in many guidelines, but this is based on limited evidence. Existing guidelines are based on the theoretical premise of toxin suppression, as many strains of S. aureus produce toxins such as leukocidins (eg, Panton-Valentine leukocidin, toxic shock syndrome toxin 1, exfoliative toxins, and various enterotoxins). Many clinicians therefore believe that limiting exotoxin production release by S. aureus could reduce its virulence and improve clinical outcomes. Clindamycin, a protein synthesis inhibitor antibiotic, is commonly used for this purpose. We report the domain-specific protocol, embedded in a large adaptive, platform trial, seeking to definitively answer this question. METHODS AND ANALYSIS: The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a pragmatic, randomized, multicenter adaptive platform trial that aims to compare different SAB therapies, simultaneously, for 90-day mortality rates. The adjunctive treatment domain aims to test the effectiveness of adjunctive antibiotics, initially comparing clindamycin to no adjunctive antibiotic, but future adaptations may include other agents. Individuals will be randomized to receive either 5 days of adjunctive clindamycin (or lincomycin) or no adjunctive antibiotic therapy alongside standard-of-care antibiotics. Most participants with SAB (within 72 hours of index blood culture and with no contraindications) will be eligible to participate in this domain. Prespecified analyses are defined in the statistical appendix to the core protocol, and domain-specific secondary analyses will be adjusted for resistance to clindamycin, disease phenotype (complicated or uncomplicated SAB) and Panton-Valentine leukocidin-positive isolate.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.326
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.346
Teacher spread0.320 · 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 designRandomized trial
Domainnot available
GenreProtocol

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
Published2024
Admission routes2
Has abstractyes

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