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Record W2606220448 · doi:10.1158/0008-5472.can-16-3217

Regulatory Aspects of Optical Methods and Exogenous Targets for Cancer Detection

2017· article· en· W2606220448 on OpenAlexaff
Willemieke S. Tummers, Jason M. Warram, Kiranya E. Tipirneni, John Fengler, Paula Jacobs, Lalitha Shankar, Lori Henderson, Betsy Ballard, T. Joshua Pfefer, Brian W. Pogue, Jamey P. Weichert, Michael Bouvet, Jonathan Sorger, Christopher H. Contag, John V. Frangioni, Michael F. Tweedle, James P. Basilion, Sanjiv S. Gambhir, Eben L. Rosenthal

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsBritish Columbia Institute of Technology
FundersNational Cancer Institute
KeywordsCancerCancer detectionComputational biologyMedicineCancer researchBiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Considerable advances in cancer-specific optical imaging have improved the precision of tumor resection. In comparison to traditional imaging modalities, this technology is unique in its ability to provide real-time feedback to the operating surgeon. Given the significant clinical implications of optical imaging, there is an urgent need to standardize surgical navigation tools and contrast agents to facilitate swift regulatory approval. Because fluorescence-enhanced surgery requires a combination of both device and drug, each may be developed in conjunction, or separately, which are important considerations in the approval process. This report is the result of a one-day meeting held on May 4, 2016 with officials from the National Cancer Institute, the FDA, members of the American Society of Image-Guided Surgery, and members of the World Molecular Imaging Society, which discussed consensus methods for FDA-directed human testing and approval of investigational optical imaging devices as well as contrast agents for surgical applications. The goal of this workshop was to discuss FDA approval requirements and the expectations for approval of these novel drugs and devices, packaged separately or in combination, within the context of optical surgical navigation. In addition, the workshop acted to provide clarity to the research community on data collection and trial design. Reported here are the specific discussion items and recommendations from this critical and timely meeting. Cancer Res; 77(9); 2197–206. ©2017 AACR.

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)0.001
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 score0.203
Threshold uncertainty score0.452

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 head0.085
GPT teacher head0.431
Teacher spread0.346 · 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.

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

Citations101
Published2017
Admission routes1
Has abstractyes

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