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Record W2222432592 · doi:10.2967/jnumed.115.158915

Successful Translation of Fluorescence Navigation During Oncologic Surgery: A Consensus Report

2015· article· en· W2222432592 on OpenAlex

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.

Bibliographic record

VenueJournal of Nuclear Medicine · 2015
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer Institute
KeywordsReimbursementMedical physicsMedicineMiamiFood and drug administrationRisk analysis (engineering)Health carePolitical science

Abstract

fetched live from OpenAlex

Navigation with fluorescence guidance has emerged in the last decade as a promising strategy to improve the efficacy of oncologic surgery. To achieve routine clinical use, the onus is on the surgical community to objectively assess the value of this technique. This assessment may facilitate both Food and Drug Administration approval of new optical imaging agents and reimbursement for the imaging procedures. It is critical to characterize fluorescence-guided procedural benefits over existing practices and to elucidate both the costs and the safety risks. This report is the result of a meeting of the International Society of Image Guided Surgery (www.isigs.org) on February 6, 2015, in Miami, Florida, and reflects a consensus of the participants' opinions. Our objective was to critically evaluate the imaging platform technology and optical imaging agents and to make recommendations for successful clinical trial development of this highly promising approach in oncologic surgery.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.264
Teacher spread0.226 · 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