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Record W3006043486 · doi:10.1039/c9an02354b

Noninvasive blood glucose detection using a quantum cascade laser

2020· review· en· W3006043486 on OpenAlexafffund
Shazzad Rassel, Chao Xu, Steven Zhang, Dayan Ban

Bibliographic record

VenueThe Analyst · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooOntario Centres of Excellence
KeywordsQuantum cascade laserPhotoacoustic spectroscopyCascadeSpectroscopyLaserMaterials sciencePhotoacoustic imaging in biomedicineOptoelectronicsOpticsBiomedical engineeringMedicineChemistryPhysicsChromatography

Abstract

fetched live from OpenAlex

A Quantum Cascade Laser (QCL) was invented in the late 90s as a promising mid-infrared light source and it has contributed to the fields of industry, military, medicine, and biology. The room temperature operation, watt-level output power, compact size, and wide tuning capability of this laser advanced the field of noninvasive blood glucose detection with the use of transmission, absorption, and photoacoustic spectroscopy. This review provides a complete overview of the recent progress and technical details of these spectroscopy techniques, using QCL as an infrared light source for detecting blood glucose concentrations in diabetic patients.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.784
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.369
Teacher spread0.331 · 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 designNot applicable
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

Citations35
Published2020
Admission routes2
Has abstractyes

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