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Record W4416209005 · doi:10.1128/spectrum.02585-25

Assessing the potential for specimen pooling to streamline nosocomial surveillance of methicillin-resistant <i>Staphylococcus aureus</i> (MRSA)

2025· article· en· W4416209005 on OpenAlexaff
Isabella Pagotto, Mohammed S. Alqahtani, Bryn K. Joy, Gregory R. McCracken, Ian Davis, Jason J. LeBlanc, Glenn Patriquin

Bibliographic record

VenueMicrobiology Spectrum · 2025
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsPoolingCefoxitinMultiplex polymerase chain reactionPolymerase chain reactionReal-time polymerase chain reactionSensitivity (control systems)

Abstract

fetched live from OpenAlex

ABSTRACT In hospitals, identification of methicillin-resistant Staphylococcus aureus (MRSA) is important to reduce possible transmissions and serious outcomes. Traditional culture and susceptibility testing requires 48–72 h, whereas Xpert MRSA polymerase chain reaction (PCR) can provide accurate MRSA detection in <1 h. Unfortunately, the high cost of such commercial PCRs precludes their use in many laboratories. Using MRSA as a model, this study hypothesized that specimen pooling in a setting of low prevalence could reduce PCR costs and provide rapid results. A total of 424 sequential nasal/groin specimens submitted for MRSA detection were subjected to routine culture-based detection using chromogenic media, and suspect colonies were confirmed using mass spectrometry and cefoxitin disk diffusion testing. These specimens were also pooled 1:8 or processed individually by Xpert MRSA PCR. Analytical sensitivity of PCR with and without pooling was compared to culture using triplicate 10-fold serial dilutions of an MRSA reference strain. The analytical sensitivity and clinical performance of specimen pooling paired with Xpert MRSA PCR were equivalent to traditional culture-based detection. Of specimen pools, 66.0% (35/53) were MRSA negative, and 34.0% (18/53) were MRSA positive. Pool resolution by PCR showed similar results as culture, identifying 116 MRSA-negative and 28 MRSA-positive specimens. At a prevalence of 6.6% (28/424), 1:8 specimen pooling with Xpert PCR provided equivalent results to culture-based methods and reduced the overall number of PCR reactions by 53.5%. Compared to individual PCR testing, specimen pooling would lower overall PCR costs, but the feasibility of this approach and the extent of benefits afforded would depend on MRSA prevalence. IMPORTANCE Identifying antibiotic-resistant bacteria like methicillin-resistant Staphylococcus aureus (MRSA) is important to prevent their spread and potentially life-threatening infections. MRSA can be detected using bacterial culture and antibiotic susceptibility testing but requires up to 3 days for results. Molecular detection methods like polymerase chain reaction (PCR) are more rapid (<1 h), but their high cost prevents implementation for many laboratories. To reduce PCR costs, specimen pooling was considered. With specimen pooling, swabs from multiple individuals are combined and tested together. If pools are negative, all their members are considered negative. If pools are positive, each swab is tested individually to identify the one(s) with MRSA. By reducing the number of PCRs required, pooling reduces PCR costs. In this study, 6.7% of samples were MRSA positive, and pooling reduced overall PCR costs by 54%, provided results in 1–2 h, and identified the same number of MRSA cases as the comparator (i.e., culture).

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)0.022
metaresearch head score (Gemma)0.023
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.311
Teacher spread0.297 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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