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Record W4385295473 · doi:10.1093/jnci/djad144

Individualized risk assessment of distant metastases in oral cavity carcinoma: a validated predictive-score model

2023· article· en· W4385295473 on OpenAlexaff
Badr Id Said, F.A. Alfaraj, Gustavo Nader Marta, Luiz Paulo Kowalski, Hugo Fontan Köhler, Shao Hui Huang, Jie Su, Wei Xu, Lawson Eng, Fábio Ynoe de Moraes, Ezra Hahn, John J. Kim, Brian O’Sullivan, Jolie Ringash, John Waldron, Leandro Luongo Matos, Eitan Prisman, Jonathan C. Irish, Christopher M. K. L. Yao, John R. de Almeida, David P. Goldstein, Andrew Hope, Ali Hosni

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsKingston General HospitalQueen's UniversityUniversity of British ColumbiaPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCohortInternal medicineFramingham Risk ScoreLymphovascular invasionOncologyCancerAJCC staging systemPathologicalRetrospective cohort studyRisk assessmentMetastasisDiseaseStaging system

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to develop and validate a risk-scoring system for distant metastases (DMs) in oral cavity carcinoma (OCC). METHODS: Patients with OCC who were treated at 4 tertiary cancer institutions with curative surgery with or without postoperative radiation/chemoradiation therapy were randomly assigned to discovery or validation cohorts (3:2 ratio). Cases were staged on the basis of tumor, node, and metastasis staging according to the eighth edition of the American Joint Committee on Cancer/Union for International Cancer Control guidelines. Predictors of DMs on multivariable analysis in the discovery cohort were used to develop a risk-score model and classify patients into risk groups. The utility of the risk classification was evaluated in the validation cohort. RESULTS: Overall, 2749 patients were analyzed. Predictors (risk score coefficient) of DMs in the discovery cohort were the following: pathological stage (p)T3-4 (0.4), pN+ (N1: 0.8; N2: 1.0; N3: 1.5), histologic grade (G) 3 (G3, 0.7), and lymphovascular invasion (0.4). The DM risk groups were defined by the sum of the following risk score coefficients: high (>1.7), intermediate (0.7-1.7), and standard risk (<0.7). The 5-year DM rates (high/intermediate/standard risk groups) were 30%/15%/4% in the discovery cohort (C-index = 0.79) and 35%/16%/5% in the validation cohort, respectively (C-index = 0.77; both P < .001). In the whole cohort, this predictive model showed excellent discriminative ability in predicting DMs without locoregional failure (29%/11%/1%), later (>2 year) DMs (11%/4%/2%), and DMs in patients treated with surgery (20%/12%/5%), postoperative radiation therapy (34%/17%/4%), and postoperative chemoradiation therapy (39%/18%/7%) (all P < .001). The 5-year overall survival rates in the overall cohort were 25%/51%/67% (P < .001). CONCLUSIONS: Patients at higher risk for DMs were identified by use of a predictive-score model for DMs that included pT3-4, pN1/2/3, G3, and lymphovascular invasion. Identified patients may be evaluated for individualized risk-adaptive treatment escalation and/or surveillance strategies.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.128
GPT teacher head0.416
Teacher spread0.289 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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