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Record W2058634279 · doi:10.1158/1538-7445.am2014-3256

Abstract 3256: Variation in treatment of severe oral dysplasia: Knowledge translation in the COOLS trial points to a pressing concern

2014· article· en· W2058634279 on OpenAlexaffabout
Miriam P. Rosin, Kitty Corbett, Huijun Jiang, Tarinee Lubpairee, Catherine F. Poh, Lewei Zhang

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsWest Fraser (Canada)Fraser Institute
Fundersnot available
KeywordsMedicineCohortDysplasiaCancerClinical trialKnowledge translationRandomized controlled trialClinical endpointSurgeryFamily medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose: Oral severe dysplasia is a high grade precancerous lesion for which there is limited knowledge on effective treatment and outcome. The Canadian Optically-guided approach for Oral Lesions Surgical (COOLS) Trial, funded by Terry Fox Research Institute, is an ongoing Phase III clinical trial assessing the clinical, molecular and cost-efficacy of fluorescence visualization (FV)-guided surgery to reduce local recurrence of high-risk oral lesions: 140 patients will have high-grade lesions, the rest invasive cancer. One critical gap, identified by COOLS’ knowledge translation component, is the lack of information on treatment practices and outcomes for patients with primary severe dysplasia outside of the trial. The purpose of this study is to establish an evidence base about variation in treatment and outcomes. Methods: We are extracting primary data on practice variation and patient outcomes from available data sources in the study sites across Canada (chart review, pathology reports and cancer registries, reports of cohort databases). This information will be supplemented via other knowledge translation methods including literature review and knowledge synthesis, and key stakeholder interviews and focus groups. Results: We report here on file review of 106 primary oral severe dysplasia in longitudinal follow-up in British Columbia (BC) for outcome outside of the COOLs trial. Follow-up was standardized for this cohort; treatment was at discretion of the attending clinician. There was considerable variability in treatment. When treated, the lesion tended to be excised conservatively before (2004), when FV began to be used in BC clinics, but wider margins were more common thereafter. When severe dysplasia were not treated (n=55), prognosis was poor: 5-year progression rate was 50.1% (31.2%-63.9%); when the lesion was removed conservatively (before 2004, n=7), the rate was worse (58.3%, Cl: 0%-84.1.1%)), and when the lesion was more aggressively removed (after 2004, n=42), the rate was significantly improved (9.4%, Cl: 0%-32.3% P = 0.002). Conclusion: A knowledge translation project turned up research-to-practice variation in the treatment of severe oral dysplasia and an association with patient outcomes. Extension of this study to all COOLS sites will assess whether such variation in surgical practice for severe oral dysplasias, and associated outcome effects, is widespread. We support greater attention to these issues, and dialogue and deliberation among key stakeholders to establish consensus about best treatment of high-grade oral dysplasias and priorities for further research. Citation Format: Miriam P. Rosin, Kitty K. Corbett, Huijun Jiang, Tarinee Lubpairee, Catherine F. Poh, Lewei Zhang. Variation in treatment of severe oral dysplasia: Knowledge translation in the COOLS trial points to a pressing concern. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3256. doi:10.1158/1538-7445.AM2014-3256

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)0.219
metaresearch head score (Gemma)0.476
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.219
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2190.476
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.005
Science and technology studies0.0020.003
Scholarly communication0.0060.002
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.311
GPT teacher head0.517
Teacher spread0.207 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes2
Has abstractyes

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