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Record W2032688406 · doi:10.1158/1538-7445.am2012-4603

Abstract 4603: Optically-guided surgical approach using fluorescence visualization can significantly improve locoregional recurrence and overall survival for early stage oral cancer

2012· article· en· W2032688406 on OpenAlexaffabout
Catherine F. Poh, Donald W. Anderson, J. Scott Durham, Kenneth W. Berean, P. Michele Williams, Calum MacAulay, Miriam P. Rosin

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsSimon Fraser UniversityBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsMedicineDysplasiaSurgeryCancerStage (stratigraphy)Subclinical infectionLesionCarcinomaBasal cellInternal medicine

Abstract

fetched live from OpenAlex

Abstract Recurrence following excision of high-grade dysplasia (HGD, severe dysplasia and carcinoma in situ) or squamous cell carcinoma (SCC) implies that the presence of subclinical changes at the margins is not apparent at surgery, resulting in incomplete excision. Fluorescence visualization (FV) has shown its value in identifying clinically not apparent high-risk oral lesions. The objective of this study is to assess the effectiveness of FV-guided surgery in reducing the loco-regional recurrence and improving overall survival. Method: From September 1st 2004 to August 31, 2009, 264 consecutive patients diagnosed with T1/T2 SCC or HGD were registered at the BC Cancer Agency and treated with surgical excision with intent to cure. Among these, 246 patients (93%; SCC, 132; HGD, 114) had a follow-up period of at least 6 months. Among these patients, 149 had the surgery done under FV guidance (FV group) while the other 97 were treated with conventional surgery procedure (control group). The outcome measurements include pathology-proven local recurrence to severe dysplasia or higher requiring another surgical procedure, regional failure, and death at follow-up. Time to outcome curve was estimated by the Kaplan-Meier method. Results: There is no significant difference between FV and control groups in age, smoking habit, lesion anatomical site, diagnosis, tumor size, and previous cancer history. There were more females in the FV group (51% vs. 33%, P = 0.006). With an average of 40 months follow-up, the FV group shows significantly lower local recurrence (7% vs. 38%), regional failure (6% vs. 23%), and death (5% vs. 20%), and significant longer time to recurrence (P < 0.0001), regional failure (P = 0.0007), and death of disease (P = 0.004). Conclusion: Although they are retrospectively collected from a single centre, the data have strongly demonstrated that the use of FV for surgical margin decision can significantly improve outcomes of the early stage oral cancer. An on-going 5-year multi-centre phase III randomized surgical trial (the COOLS trial), funded by Terry Fox Research Institute, has started to recruit patients and the data will be used to validate the results of this study. (Supported by R01 DE17013 from the National Institute of Dental and Craniofacial Research, CCSRI-20336 from Canadian Cancer Society Research Institute, and TFRI-2009-24 from Terry Fox Research Institute. CFP is currently supported by a Scholar Award from the Michael Smith Foundation for Health Research). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4603. doi:1538-7445.AM2012-4603

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.260
GPT teacher head0.479
Teacher spread0.219 · 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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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