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Record W3111215819 · doi:10.1093/ofid/ofaa609

Impact of Rejection of Low-Quality Wound Swabs on Antimicrobial Prescribing: A Controlled Before–After Study

2020· article· en· W3111215819 on OpenAlexafffund
Xavier Marchand-Senécal, Ian Brasg, Robert Kozak, Marion Elligsen, Christie Vermeiren, Antoine Corbeil, Kevin R. Barker, Kevin Katz, Jeff Powis, Wayne L. Gold, Jerome A. Leis

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity Health NetworkToronto East General HospitalUniversity of TorontoHumber River Regional HospitalHealth Sciences CentreSunnybrook Health Science Centre
FundersUniversity of Toronto
KeywordsMedicineAntimicrobialWorkloadAntibioticsMetric (unit)Intensive care medicineMicrobiologyOperations management

Abstract

fetched live from OpenAlex

In this controlled before-after study, wound swabs were only processed for culture, identification, and susceptibility testing if a quality metric, determined by the Q score, was met. Rejection of low-quality wound swabs resulted in a modest decrease in reflexive antibiotic initiation while reducing laboratory workload and generating few clinician requests.

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)0.020
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.027
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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 designNon-randomized trial
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

Citations11
Published2020
Admission routes2
Has abstractyes

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