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Record W2399057498 · doi:10.3390/mi7060094

The GenePOC Platform, a Rational Solution for Extreme Point-of-Care Testing

2016· review· en· W2399057498 on OpenAlexaff
Luc Bissonnette, Michel G. Bergeron

Bibliographic record

VenueMicromachines · 2016
Typereview
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPoint-of-care testingRisk analysis (engineering)Intensive care medicineNatural disasterHealth careEngineeringMedicineComputer scienceGeographyEconomicsPathology

Abstract

fetched live from OpenAlex

Extreme point-of-care (POC) testing for infections, as performed (endured) in low-resource settings, developing countries, tropical areas, or in conditions following emergency crises or natural disasters, must be undertaken under environmental, logistic, and societal conditions which impose a significant deal of stress on local human populations and healthcare providers. For disease diagnostics or management, simple and robust biomedical equipment and reagents are required and needed. This chapter aims to overview some of these stresses (requirements) and intends to describe some of the solutions already engineered at the heart of centripetal (centrifugal) microfluidic platforms such as that of GenePOC Inc. to enable rapid, robust, and reproducible nucleic acid-based diagnostics of infectious diseases, to better control the morbidity and mortality of infections and the expanding threat posed by antimicrobial resistance.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.282
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations27
Published2016
Admission routes1
Has abstractyes

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