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Record W2085840824 · doi:10.1002/hed.20173

Dynamic MR lymphangiography and carbon dye for sentinel lymph node detection: A solution for sentinel lymph node biopsy in mucosal head and neck cancer

2005· article· en· W2085840824 on OpenAlexaff
Richard W. Nason, Mark G. Torchia, Carmen Morales, James A. Thliveris

Bibliographic record

VenueHead & Neck · 2005
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineSentinel lymph nodeBiopsyLymphLymph nodeSentinel nodeLymphatic systemNeck dissectionGamma probeRadiologyHead and neck cancerHead and neckCarcinomaPathologyCancerSurgeryRadiation therapyBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The practical application of sentinel lymph node biopsy in squamous cell carcinoma of the head and neck is restricted by the time sensitivity of blue dye and lack of spatial resolution and nonspecific node enhancement with radiocolloid. This study evaluates the use of magnetic resonance (MR) lymphangiography and carbon dye labeling to circumvent these limitations. METHODS: Gadomer/carbon dye mixture was injected into the tongue and stifle of adult swine (n = 4). MR lymphatic mapping was followed by intraoperative mapping with isosulfan blue dye. Sentinel lymph node biopsy and completion node dissection were performed 60 minutes after injection in four nodal basins and at 7 days after injection in eight. RESULTS: The technique was successful in all 12 nodal basins. The sentinel lymph nodes were stained black at the time of the immediate and delayed dissections. CONCLUSIONS: MR lymphangiography provides temporal and anatomic localization of the sentinel lymph node with a single investigation. Carbon dye is a sensitive and persistent visual marker of MRI-targeted sentinel lymph nodes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

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 head0.302
Teacher spread0.283 · 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.

Study designObservational
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

Citations32
Published2005
Admission routes1
Has abstractyes

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