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Record W4247619157 · doi:10.1158/1538-7445.am2019-4223

Abstract 4223: Conducting community oral cancer screening among South Asians in British Columbia

2019· article· en· W4247619157 on OpenAlexaffabout
Leigha D. Rock, Madhurima Datta, Denise M. Laronde, Anita Carraro, Jagoda Korbelik, Alan Harrison, Martial Guillaud

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsBC Cancer AgencySpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsMedicineCancerArecaStage (stratigraphy)PathologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction: Globally, more than 300,000 cases of oral cancer are diagnosed annually. South Asian countries, such as India, bear the brunt of this disease due to rampant use of chewing tobacco, betel quid and areca nut. BC has a high proportion of South Asian immigrants. Oral cancer has a high mortality rate (~50% 5-year survival) due to the advanced stage at which it is often diagnosed. It is purported that the majority of oral cancers develop from oral potentially malignant lesions (OPML). While lesions can be easily detected by oral health care providers, it can be challenging to differentiate benign lesions from OPML. DNA aneuploidy has been shown to be an effective marker to predict malignant transformation in OPML. Quantitative cytology (QC) studies DNA content (ploidy) and nuclear morphometric changes within the cell. The aim of this project is to assess the need for oral cancer screening in South Asians in BC and to validate QC as an adjunct screening device in a predominantly South Asian community screening setting to assess its effectiveness in identifying high-risk lesions among visually suspicious lesions. Methods: Demographic information (gender, age, country of birth, ethnicity, risk habit information and dental usage) were collected at the time of screening. Extraoral, intraoral and fluorescence visualization (FV) examinations were conducted. Buccal mucosal brushings were collected from each participant. Brushings were also collected from lesions or areas that had a loss of fluorescence. Thin-layer cytology slides were prepared and stained using Feulgen Eosin. Slides were scanned using the Cancer Imaging Scanner at BC Cancer using machine learning classification algorithms to identify single, in-focus epithelial nucleus. Cells are classified based on DNA ploidy and malignant associated changes. Results: 307 participants were screened of which 303 were eligible. More than 99% of the participants were South Asian or Asian. 104 (34%) lesions were documented: 45 (15%) were high risk (white or red lesions, lichen planus (LP)) and 59 (19%) were low risk (trauma, candidiasis, aphthous ulcers). Twenty participants (7%) were suspected to have high-risk OPMLs (not LP): 12 were referred directly to our Next Gen Oral Dysplasia clinic for biopsy, while 8 were reassessed at 3 weeks. Chewing tobacco was found to be strongly associated with lesion presence (p<0.01). QC analysis is ongoing for 320 samples. To date, 5 biopsies have been performed resulting in 1 mild dysplasia, 1 severe dysplasia and 3 hyperplasia. Conclusion: South Asians in BC were found to be at high risk for OPMLs. QC may help to improve the sensitivity and specificity of oral cancer screening by distinguishing false FV positive inflammatory lesions from high-risk lesions. Citation Format: Leigha D. Rock, Madhurima Datta, Denise M. Laronde, Anita Carraro, Jagoda Korbelik, Alan Harrison, Martial Guillaud. Conducting community oral cancer screening among South Asians in British Columbia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4223.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.275
GPT teacher head0.480
Teacher spread0.206 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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