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Record W2140384607 · doi:10.1128/jcm.01253-07

Reductions in Workload and Reporting Time by Use of Methicillin-Resistant <i>Staphylococcus aureus</i> Screening with MRSA <i>Select</i> Medium Compared to Mannitol-Salt Medium Supplemented with Oxacillin

2008· article· en· W2140384607 on OpenAlexaff
Philippe Lagacé‐Wiens, Michelle J. Alfa, Kanchana Manickam, G. K. M. Harding

Bibliographic record

VenueJournal of Clinical Microbiology · 2008
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of ManitobaShared HealthSt. Boniface Hospital
Fundersnot available
KeywordsStaphylococcus aureusMedicineMethicillin-resistant Staphylococcus aureusTurnaround timeMannitolStaphylococcal infectionsMicrococcaceaeMicrobiologyMeticillinWorkloadInfection controlAntibacterial agentSurgeryBiologyAntibioticsBacteria

Abstract

fetched live from OpenAlex

Methicillin-resistant Staphylococcus aureus (MRSA) is a significant pathogen in both nosocomial and community settings, and screening for carriers is an important infection control practice in many hospitals. In this retrospective study, we demonstrate that the implementation of an MRSA screening protocol using a selective chromogenic medium (MRSASelect) reduced the workload for this screening test by 63.7% overall and by 12.6% per specimen and reduced the turnaround time for reporting by an average of 1.33 days for all MRSA screening specimens, 1.97 days for MRSA-positive specimens, and 1.3 days for MRSA-negative specimens compared to standard mannitol-salt agar supplemented with 6 mg of oxacillin/liter.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.079
GPT teacher head0.357
Teacher spread0.278 · 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 designBench or experimental
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

Citations17
Published2008
Admission routes1
Has abstractyes

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