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Record W2744098260 · doi:10.1016/j.jiph.2017.07.009

Rectal swab screening assays of public health importance in molecular diagnostics: Sample adequacy control

2017· article· en· W2744098260 on OpenAlexafffund
Sanja Glisovic, Shaun Eintracht, Yves Longtin, Matthew Oughton, Ivan Brukner

Bibliographic record

VenueJournal of Infection and Public Health · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsPolymerase chain reactionFecesMedicine16S ribosomal RNAInfection controlBiologyMicrobiologyBacteriaPathologyGeneticsGene

Abstract

fetched live from OpenAlex

Rectal swabs are routinely used by public health authorities to screen for multi-drug resistant enteric bacteria including vancomycin-resistant enterococci (VRE) and carbapenem-resistant enterobacteriaceae (CRE). Screening sensitivity can be influenced by the quality of the swabbing, whether performed by the patient (self-swabbing) or a healthcare practitioner. One common exclusion criterion for rectal swabs is absence of "visible soiling" from fecal matter. In our institution, this criterion excludes almost 10% of rectal swabs received in the microbiology laboratory. Furthermore, over 30% of patients in whom rectal swabs are cancelled will not be re-screened within the next 48h, resulting in delays in removing infection prevention measures. We describe two quantitative polymerase chain reaction (qPCR)-based assays, human RNAse P and eubacterial 16S rDNA, which might serve as suitable controls for sampling adequacy. However, lower amounts of amplifiable human DNA make the 16s rDNA assay a better candidate for sample adequacy control.

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)0.030
metaresearch head score (Gemma)0.055
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.055
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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