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Record W2561399765 · doi:10.5772/65740

A Real-Time PCR-Based Diagnostic Test for Organisms in Respiratory Tract Infection

2016· book-chapter· en· W2561399765 on OpenAlexaff
Takashi Hirama

Bibliographic record

VenueInTech eBooks · 2016
Typebook-chapter
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsToronto General HospitalHospital for Sick Children
Fundersnot available
KeywordsRespiratory tractPneumoniaPathogenRespiratory tract infectionsDiagnostic testBiologyMedicineRespiratory systemIntensive care medicineMicrobiologyInternal medicineVeterinary medicine

Abstract

fetched live from OpenAlex

Respiratory tract infection, especially pneumonia, is a significant cause of morbidity and mortality worldwide. Although rapid and accurate identification of the pathogens and the corresponding treatment, which is based on the microbiological results, is required in the healthcare setting, the current clinical tests lack high sensitivity and flexibility. As of yet, a comprehensive approach has not been able to work these issues out. Meanwhile, the development of molecular techniques enables the detection of organisms from respiratory specimens speedily as well as precisely and aids the settlement of such issues. With our novel approach that employs relative quantification, we successfully set the cutoff value to discriminate the causative pathogen from colonizing commensal organisms by real-time PCR. In this way, a diagnostic system for respiratory pathogens was devised and validated through clinical sample testing. In this chapter, a real-time PCR-based test capable of differentiating causative pathogens in respiratory specimens is described, and also its principle and the utility of this approach are illustrated.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.014

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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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