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Record W3095898966 · doi:10.1038/s41598-020-75037-1

Comparison of near infrared spectroscopy (NIRS) and near-infrared transillumination-backscattering sounding (NIR-T/BSS) methods

2020· article· en· W3095898966 on OpenAlexafffund
Agnieszka Gruszecka, Marcin Gruszecki, J. Patrick Neary, Jyotpal Singh, Taylor A. Teckchandani, Monika Waśkow, Magdalena Wszędybył-Winklewska, Wojciech Gumiński, Andrzej F. Frydrychowski, Jacek Rumiński, Piotr Lass, Gregory P. Krätzig, Paweł J. Winklewski

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsRoyal Canadian Mounted PoliceUniversity of Regina
FundersNarodowe Centrum NaukiUniversity of Regina
KeywordsNear-infrared spectroscopyTransilluminationMedicineNuclear magnetic resonanceBiomedical engineeringNuclear medicinePathologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The aim of the study was to compare simultaneously recorded a NIR-T/BSS and NIRS signals from healthy volunteers. NIR-T/BSS is a device which give an ability to non-invasively detect and monitor changes in the subarachnoid space width (SAS). Experiments were performed on a group of 30 healthy volunteers (28 males and 2 females, age 30.8 ± 13.4 years, BMI = 24.5 ± 2.3 kg/m 2 ). We analysed recorded signals using analysis methods based on wavelet transform (WT) for the wide frequency range from 0.0095 to 2 Hz. Despite the fact that both devices use a similar radiation source both signals are distinct from each other. We found statistically significant differences for WT amplitude spectra between both signals. Additionally, we showed different relationships of both signals to blood pressure. Collectively, based on the present findings and those of previous studies, we can conclude that the combination of NIR-T/BSS or NIRS signals and time–frequency analysis opens new frontiers in science, and give possibility to understand and diagnosis of various neurodegenerative and ageing related diseases to improve diagnostic procedures and patient prognosis.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.396
Teacher spread0.359 · 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 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

Citations10
Published2020
Admission routes2
Has abstractyes

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