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Record W3025376336 · doi:10.3389/fphys.2020.00339

Optical Biopsy of the Upper GI Tract Using Fluorescence Lifetime and Spectra

2020· article· en· W3025376336 on OpenAlexafffund
Zhaojun Nie, Shu-Chi Yeh, Michelle LePalud, Fares Badr, Frances Tse, David Armstrong, Louis W. C. Liu, M. Jamal Deen, Qiyin Fang

Bibliographic record

VenueFrontiers in Physiology · 2020
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsUniversity of TorontoPopulation Health Research InstituteMcMaster University
FundersCanadian Cancer Society Research InstituteNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Centres of Excellence
KeywordsBiopsyEndoscopeEsophagusFluorescenceDuodenumSampling (signal processing)StomachEndoscopyMedicinePathologyRadiologyGastroenterologyInternal medicineComputer scienceOptics

Abstract

fetched live from OpenAlex

Screening and surveillance for gastrointestinal (GI) cancers by endoscope guided biopsy are invasive, time-consuming, and has the potential for sampling error. Endogenous fluorescence spectra of tissue containing biochemical and physiological information that may enable real-time, objective diagnosis when combined with endoscopic images. We first briefly reviewed optical biopsy modalities for GI cancer diagnosis with a focus on fluorescence-based techniques. In an ex vivo pilot clinical study, we measured fluorescence spectra and lifetime on fresh biopsy specimens obtained during routine upper GI screening procedures. Our results demonstrated the feasibility of rapid acquisition of time-resolved fluorescence spectra from fresh GI mucosal specimens. We also identified spectroscopic signatures that differentiate normal mucosa from the esophagus, stomach, and duodenum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.290
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2020
Admission routes2
Has abstractyes

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