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Record W4309579149 · doi:10.1016/j.surg.2022.06.036

Use of fluorescence imaging and indocyanine green for sentinel node mapping during gastric cancer surgery: Results of an intercontinental Delphi survey

2022· article· en· W4309579149 on OpenAlexaff
Danny A. Sherwinter, Luigi Boni, Michael Bouvet, Lorenzo Ferri, Woo Jin Hyung, Takeaki Ishizawa, Ronald Kaleya, Kaitlyn J. Kelly, Norihiro Kokudo, Enrique Lanzarini, Misha Luyer, Norio Mitsumori, Carmen Mueller, Doo Joong Park, Dario Ribero, Riccardo Rosati, Jelle P. Ruurda, Meindert N. Sosef, Sylke Schneider-Koraith, Giuseppe Spinoglio, Vivian E. Strong, Naoto Takahashi, Hiroya Takeuchi, Bas P. L. Wijnhoven, Han‐Kwang Yang, Fernando Dip, Emanuele Lo Menzo, Kevin P. White, Raúl J. Rosenthal

Bibliographic record

VenueSurgery · 2022
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsMcGill University
FundersNational Cancer Institute
KeywordsIndocyanine greenMedicineSentinel lymph nodeSentinel nodeLymphLymph nodeDissection (medical)RadiologySurgeryCancerPathologyBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding the extent of tumor spread to local lymph nodes is critical to managing early-stage gastric cancer. Recently, fluorescence imaging with indocyanine green has been used to identify and characterize sentinel lymph nodes during gastric cancer surgery, but no published guidelines exist. We sought to identify areas of consensus among international experts in the use of fluorescence imaging with indocyanine green for mapping sentinel lymph nodes during gastric-cancer surgery. METHODS: In this 2-round, online Delphi survey, 27 international experts voted on 79 statements pertaining to patient preparation and contraindications to fluorescence imaging with indocyanine green during gastric cancer surgery; indications; technical aspects; advantages/disadvantages and limitations; and training and research. Methodological steps were adopted during survey design to minimize bias. RESULTS: Consensus was reached on 61 of 79 statements, including giving single injections of indocyanine green into each of the 4 quadrants peritumorally, administering indocyanine green on the same day as surgery, injecting a total of 1 to 5 mL of 5 mg/mL indocyanine green, injecting endoscopically into submucosa, and repeating indocyanine green injections a second time if sentinel lymph node visualization remains inadequate. Consensus also was reached that fluorescence imaging with indocyanine green is an acceptable single-agent modality for sentinel lymph node identification and that the sentinel lymph node basin method is preferred. However, sentinel lymph node dissection should be limited to T1 gastric cancer and tumors ≤4 cm in diameter, and further research is necessary to optimize the technique and render fluorescence-guided sentinel lymph nodes dissection acceptable for routine clinical use. CONCLUSION: Although considerable consensus was achieved, further research is necessary before this technology should be used in routine practice.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.061
GPT teacher head0.281
Teacher spread0.220 · 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 designQualitative
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

Citations19
Published2022
Admission routes1
Has abstractno

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