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Molecular methods used in clinical laboratory: prospects and pitfalls

2007· review· en· W2062739222 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFEMS Immunology & Medical Microbiology · 2007
Typereview
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsBC Centre for Disease ControlUniversity of British Columbia
FundersCenters for Disease Control and Prevention
KeywordsBorrelia burgdorferiBiologyMolecular diagnosticsBorreliaTreponemaPathogenLyme diseaseIdentification (biology)Clinical microbiologySyphilisMolecular epidemiologyDiagnostic testVirologyComputational biologyMicrobiologyImmunologyBioinformaticsMedicineGeneticsHuman immunodeficiency virus (HIV)EcologyVeterinary medicine

Abstract

fetched live from OpenAlex

The role of molecular detection, identification and typing or fingerprinting of microorganisms has shifted gradually from the academic world to the routine diagnostic laboratory. Molecular methods have been used increasingly over the past decade to improve the sensitivity, specificity and turn-around time in the clinical laboratory. Molecular methods have also been used to identify new and nonculturable agents. Many high-throughput molecular tests are now available commercially, which impacts on the infrastructure in many of the diagnostic laboratories. In this paper, we take an overall look at the use of molecular methods (prospects vs. pitfalls) based on our clinical and public health experience, particularly as they related to Borrelia burgdorferi, a vector-borne pathogen, Treponema pallidum, a re-emerging sexually transmitted global pathogen, and West Nile virus, a newly recognized virus in North America.

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0040.004
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 head0.064
GPT teacher head0.468
Teacher spread0.403 · 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