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Record W3176573047 · doi:10.1177/2513826x211022209

Failure of Radiotracer Migration: Salvaging Sentinel Lymph Node Biopsy in Melanoma Care With Indocyanine Green

2021· article· en· W3176573047 on OpenAlex
Christine Nicholas, Carmen Webb, Claire Temple‐Oberle

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenuePlastic Surgery Case Studies · 2021
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIndocyanine greenMedicineSentinel lymph nodeSentinel nodeBiopsyMelanomaRadiologySurgeryInternal medicineCancerBreast cancerCancer research

Abstract

fetched live from OpenAlex

Reducing false negative rates for sentinel lymph node biopsies (SLNB) in melanoma is important to accurately prognosticate and to guide treatment. Traditionally, SLNB has been performed with the adjunct of radiotracers and blue dye. Although sentinel node mapping is highly successful in axillary and inguinal node basins, identification of nodes in the head and neck is not as accurate with traditional methods. One reason for this may be failure of radiotracer migration. To augment standard technique using a radiocolloid, indocyanine green (ICG) combined with near infrared spectroscopy (NIRS), has shown promising results. We demonstrate a case of an individual undergoing SLNB in the head and neck region with failure of radiotracer migration. Identification of a sentinel node was accomplished with the use of ICG and NIRS. This technology offers an opportunity to salvage the SLNB when traditional methods fail.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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