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Record W2784657126 · doi:10.1364/boe.9.000801

Surface-enhanced Raman scattering for the detection of polycystic ovary syndrome

2018· article· en· W2784657126 on OpenAlex
Ali Momenpour, Patrícia Lima, Yi-An Chen, Chii‐Ruey Tzeng, Benjamin K. Tsang, Hanan Anis

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomedical Optics Express · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNational Health Research InstitutesAcademia SinicaCanadian Institutes of Health ResearchMinistry of Science and Technology
KeywordsPolycystic ovaryPartial least squares regressionPrincipal component analysisRaman scatteringChemerinMedicineInternal medicineEndocrinologyMaterials scienceRaman spectroscopyDiabetes mellitusOpticsPhysicsInsulin resistanceMathematicsComputer scienceArtificial intelligenceStatisticsAdipokine

Abstract

fetched live from OpenAlex

Polycystic ovary syndrome (PCOS) is a multi-factorial heterogeneous syndrome that affects many women of reproductive age. This work demonstrates how the surface-enhanced Raman scattering (SERS) technique can be used to differentiate between PCOS and non-PCOS patients. We determine that the use of SERS, in conjunction with partial least squares (PLS) and principal component analysis (PCA), allows us to detect PCOS in patient samples. Although the role of chemerin in the pathogenesis of PCOS patients is not clear, this work enables us to measure their chemerin levels using the PLS regression method.

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 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 score0.454
Threshold uncertainty score0.479

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.313
Teacher spread0.298 · 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