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Record W2965606034 · doi:10.1117/1.jbo.24.7.075003

Development of a near-infrared spectroscopy interface able to assess oxygen recovery kinetics in the right and left sides of the pelvic floor

2019· article· en· W2965606034 on OpenAlexafffund
Andrew Macnab, Lynn Stothers, Emily G. Deegan

Bibliographic record

VenueJournal of Biomedical Optics · 2019
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversity of British Columbia
FundersRick Hansen Institute
KeywordsPelvic floorMedicineBiomedical engineeringAsymptomaticOxygenationNuclear medicineUrologyMaterials scienceInternal medicineAnatomy

Abstract

fetched live from OpenAlex

Near-infrared spectroscopy (NIRS) muscle oxygenation data are relied on in sports medicine. Many women with urinary incontinence (UI) have dysfunctional pelvic floor muscles (PFMs) but their evaluation lacks such measures; a transvaginal NIRS interface would enable the PFM to be interrogated. Paired miniature fiber-optic cables were configured on a rigid foam insert so their emitter detector arrays with an interoptode distance of 20 mm apposed the right and left inner sides of a disposable clear plastic vaginal speculum, and linked to a standard commercial NIRS instrument. Measurement capability was assessed through conduct of three maximum voluntary contractions (MVCs) and one sustained maximum voluntary contraction of the PFM with calculation of HbDiff (½RT), a validated muscle reoxygenation kinetic parameter. In all four asymptomatic controls, mean age 40, mean BMI 21.4, MVCs were associated with changes in PFM oxyhemoglobin (O2Hb), deoxyhemoglobin (HHb) concentration, and their difference (HbDiff) comparable to those in voluntary muscle sports medicine studies. NIRS data during recovery (reoxygenation) allowed calculation of HbDiff (½RT). New techniques are called for to evaluate UI. This NIRS interface warrants further development as the provision of quantitative reoxygenation kinetics offers more comprehensive evaluation of patients with PFM dysfunction.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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