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Record W2168840959 · doi:10.1002/lsm.10084

Raman spectroscopy for optical diagnosis in normal and cancerous tissue of the nasopharynx—preliminary findings

2003· article· en· W2168840959 on OpenAlexafffund
David P. Lau, Zhiwei Huang, Harvey Lui, Chris S. Man, Ken Berean, Murray Morrison, Haishan Zeng

Bibliographic record

VenueLasers in Surgery and Medicine · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsBC Cancer AgencyVancouver General HospitalUniversity of British Columbia
FundersNational Cancer InstituteNational Medical Research CouncilFondation pour la Recherche MédicaleCanadian Dermatology FoundationDermatology Foundation
KeywordsRaman spectroscopyPathologyLaserCancerIn vivoNuclear magnetic resonanceMedicineSpectroscopyChemistryNuclear medicineOpticsInternal medicineBiologyPhysics

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Raman spectroscopy (RS), which can detect molecular changes associated with cancer, was explored as a means of distinguishing normal and cancerous nasopharyngeal tissue. STUDY DESIGN/PATIENTS AND METHODS: Tissue from six patients with normal and cancerous biopsies was studied using a rapid acquisition Raman spectrometer. RESULTS: Spectra were obtainable within 5 seconds. Consistent differences were noted between normal and cancer tissue in three bands 1,290-1,320 cm(-1) (P = 0.005), 1,420-1,470 cm(-1) (P = 0.006), and 1,530-1,580 cm(-1) (P = 0.002). CONCLUSIONS: Spectral differences appear to exist between normal and cancerous nasopharyngeal tissue. The ability to obtain spectra rapidly supports the potential for future in vivo application.

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)0.001
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 score0.251
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.314
Teacher spread0.301 · 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

Citations179
Published2003
Admission routes2
Has abstractyes

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