MétaCan
Menu
Back to cohort
Record W2336692385 · doi:10.1364/cancer.2016.jth3a.3

Multi-excitation, Multi-emission Autofluorescence Imaging (AFI) for the In Vivo Identification of at Risk Cervical Tissue

2016· article· en· W2336692385 on OpenAlexaff
Calum MacAulay, Dennis D. Cox, Pierre Lane, E. Neely Atkinson, José‐Miguel Yamal, Leonid Fradkin, Daniel Serachitopol, Sylvia Lam, Hamid Pahlevaninezhad, Anthony Lee, Rashika Raizada, Dianne Miller, Jessica N. McAlpine, Thomas Ehlen, Sarah Finlayson, Janice S. Kwon, Marette Lee, Christina Gutierrez, Zuber D. Mulla, Colin Schlosser, Kayla Castaneda, Felipe Gómez Castañeda, Michele Follen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsVancouver General HospitalOccupational Cancer Research Centre
Fundersnot available
KeywordsAutofluorescenceMedicineConfoundingIn vivoExcitationCervixPathologyOpticsFluorescenceCancerInternal medicineBiologyPhysics

Abstract

fetched live from OpenAlex

In the cervix in vivo multi-excitation multi-emission (AFI) data can have high sensitivity (~90% ) but specificity is affected by confounding tissue structures. OCT-AFI combined imaging has potential to identify these confounding structures.

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 score0.002
Threshold uncertainty score0.007

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicPhotoacoustic and Ultrasonic ImagingFrench-language works237,207