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Record W2089380562 · doi:10.1353/hpu.2007.0106

Oral Cancer Screening in a High-Risk Underserved Community: Vancouver Downtown Eastside

2007· article· en· W2089380562 on OpenAlexafffundabout
Catherine F. Poh, Gregory Hislop, Brenda Currie, Sean Sikorski, Chris Zed, Lewei Zhang, Calum MacAulay, Miriam P. Rosin

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2007
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health Research
KeywordsOutreachDowntownMedicineCancerFamily medicinePopulationGerontologyEnvironmental healthInternal medicinePathology

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate a clinic-centered oral cancer screening initiative in one of the poorest communities in Canada, assessing the need for screening and the acceptance of screening and identifying hindrances to both screening and follow-up. The study group included 204 people in the Vancouver Downtown Eastside (DTES). This was shown to be a high-risk community based on risk factors, lack of access to care, and the high frequency of oral mucosal anomalies. Acceptance of screening was high (98%); however, acceptance of biopsy for abnormal findings and follow-up was low. Among the 12 patients with clinical leukoplakia who were biopsied, 10 showed cancer or precancer. In summary, these data show a need for screening in this population and an ability to recruit patients to screening. They support a future expansion of this initiative to create a more comprehensive strategy for outreach to this underserved community that extends to screening, diagnostic work-up, and treatment.

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)0.001
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 score0.898
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.071
GPT teacher head0.369
Teacher spread0.297 · 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

Citations30
Published2007
Admission routes3
Has abstractyes

Explore more

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