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Record W3006848801 · doi:10.1039/d0lc00047g

Towards practical sample preparation in point-of-care testing: user-friendly microfluidic devices

2020· review· en· W3006848801 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

VenueLab on a Chip · 2020
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsPoint-of-care testingMicrofluidicsPoint of careSample (material)User FriendlySample preparationPoint (geometry)Environmentally friendlyNanotechnologyComputer scienceProcess engineeringMaterials scienceEngineeringMedicineChromatographyChemistryOperating systemNursingBiologyMathematicsPathology

Abstract

fetched live from OpenAlex

Microfluidic technologies offer a number of advantages for sample preparation in point-of-care testing (POCT), but the requirement for complicated external pumping systems limits their wide use. To facilitate sample preparation in POCT, various methods have been developed to operate microfluidic devices without complicated external pumping systems. In this review, we introduce an overview of user-friendly microfluidic devices for practical sample preparation in POCT, including self- and hand-operated microfluidic devices. Self-operated microfluidic devices exploit capillary force, vacuum-driven pressure, or gas-generating chemical reactions to apply pressure into microchannels, and hand-operated microfluidic devices utilize human power sources using simple equipment, including a syringe, pipette, or simply by using finger actuation. Furthermore, this review provides future perspectives to realize user-friendly integrated microfluidic circuits for wider applications with the integration of simple microfluidic valves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.312
Teacher spread0.279 · 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