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Record W1996553827 · doi:10.1002/hed.21114

Nodal ratio as an independent predictor of survival in squamous cell carcinoma of the oral cavity

2009· article· en· W1996553827 on OpenAlexaff
Mark G. Shrime, Gideon Bachar, Jane Lea, Cheryl Volling, Clement Ma, Patrick Gullane, Ralph Gilbert, Jonathan C. Irish, Dale Brown, David P. Goldstein

Bibliographic record

VenueHead & Neck · 2009
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsNODALMedicineInternal medicineOncologyCarcinomaBasal cellMultivariate analysisSurvival analysisUnivariate analysisNodal analysis

Abstract

fetched live from OpenAlex

BACKGROUND: The association between nodal ratio and survival in oral cavity carcinomas has recently been proposed, but no prospective evaluations exist. METHODS: We sought to determine, using an institutional database, whether nodal ratio impacts survival in node-positive oral cavity squamous cell carcinoma. RESULTS: Between 1994 and 2004, 143 new diagnoses of N(1-2) squamous cell carcinoma of the oral cavity were identified. The mean number of nodes identified was 41.6, and the mean nodal ratio was 9%. Nodal ratio was strongly statistically associated with overall and disease-specific survival in both univariate and multivariate analyses. No other prognostic indicator maintained that degree of statistical significance. Patients could be stratified into low (0% to 6%), moderate (6% to 13%), and high-risk (>13%) groups based on nodal ratio. CONCLUSIONS: In squamous cell carcinoma of the oral cavity, an increased nodal ratio is a strong predictor of decreased survival. Risk of death can be stratified by nodal ratio.

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)0.000
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 score0.007
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.298
Teacher spread0.272 · 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

Citations90
Published2009
Admission routes1
Has abstractyes

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