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Record W2146316017 · doi:10.1186/1916-0216-42-28

The impact of clinical versus pathological staging in oral cavity carcinoma–a multi-institutional analysis of survival

2013· article· en· W2146316017 on OpenAlexaffabout
Vincent L. Biron, Daniel A. O’Connell, Hadi Seikaly

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsPathologicalMedicineStage (stratigraphy)Univariate analysisMultivariate analysisProportional hazards modelCarcinomaPathological stagingSurvival analysisInternal medicineT-stageRadiologySurvival rateUnivariateGastroenterologyOncologyOverall survivalMultivariate statistics

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate any disparity in clinical versus pathological TNM staging in oral cavity squamous cell carcinoma (OCSCC) patients and any impact of this on survival. DESIGN: Demographic, survival, staging, and pathologic data on all patients undergoing surgical treatment for OCSCC in Alberta between 1998 and 2006 was collected. Clinical and pathological TNM staging data were compared. Patients were stratified as pathologically downstaged, upstaged or unchanged. SETTING: Tertiary care centers in Alberta, Canada. MAIN OUTCOME MEASURES: Survival differences between groups were analyzed using Kaplan-Meier and Cox regression models. RESULTS: Patients with clinically early stage tumors were pathologically upstaged in 21.9% of cases and unchanged in 78.1% of cases. Patients with clinically advanced stage tumors were pathologically downstaged in 7.9% of cases and unchanged in 92.1% of cases. Univariate and multivariate estimates of disease-specific survival showed no statistically significant differences in survival when patients were either upstaged or downstaged. CONCLUSIONS: Some disparity exists in clinical versus pathological staging in OCSCC, however, this does not have any significant impact on disease specific survival.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.106
GPT teacher head0.398
Teacher spread0.293 · 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

Citations33
Published2013
Admission routes2
Has abstractyes

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