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Record W3197454097 · doi:10.1002/hed.26851

Identification of specific clinical risk factors associated with the malignant transformation of oral epithelial dysplasia

2021· article· en· W3197454097 on OpenAlexaff
Justin Kierce, Yuliang Shi, Hagen Klieb, Nick Blanas, Wei Xu, Marco Magalhaes

Bibliographic record

VenueHead & Neck · 2021
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsPublic Health OntarioHealth Sciences CentreUniversity of WaterlooSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineMalignant transformationDysplasiaLeukoplakiaEpithelial dysplasiaImmunosuppressionBiopsyCancerDermatologyInternal medicineOncologyPathology

Abstract

fetched live from OpenAlex

Abstract Background Factors that increase the risk of malignant transformation of oral epithelial dysplasia (OED) are not completely elucidated. Methods A retrospective chart review was performed assessing risk factors for transformation of OED, and cancer staging for transformed cases at Sunnybrook Health Sciences Centre. Results Two‐hundred four patients were diagnosed with OED, and 16.7% (34) underwent malignant transformation. Risk factors associated with transformation included: heavy tobacco smoking, excessive EtOH consumption, non‐homogenous leukoplakia, size >200 mm 2 , moderate dysplasia or greater than moderate, progression of dysplasia grades, and immunosuppression. Transformed cases followed for a dysplastic lesion were associated with a stage‐I cancer diagnosis, and cancer cases with no prior biopsy were associated with a stage‐IV diagnosis. Conclusions In addition to commonly cited risk factors, immunosuppression was associated with malignant transformation, including the use of topical steroids. Analyzing risk factors can help clinicians define risk of progression in patients with OED.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.277

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 head0.067
GPT teacher head0.355
Teacher spread0.288 · 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 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

Citations15
Published2021
Admission routes1
Has abstractyes

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