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Record W2162987544 · doi:10.1002/hed.23001

Outcomes of squamous cell cancer of the oral tongue managed at the princess margaret hospital

2012· article· en· W2162987544 on OpenAlexaff
David P. Goldstein, Gideon Bachar, Jane Lea, Mark G. Shrime, Rajan S. Patel, Patrick Gullane, Dale Brown, Ralph Gilbert, John Kim, Jonathan Waldron, Bayardo Perez‐Ordoñez, Aileen M. Davis, Lu Cheng, Wei Xu, Jonathan C. Irish

Bibliographic record

VenueHead & Neck · 2012
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of TorontoToronto General HospitalPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineTongueProportional hazards modelBasal cellInternal medicineCancerSurvival analysisHead and neck cancerRadiation therapySurgeryHead and neckLog-rank testOncologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to analyze the outcomes and treatment in patients with squamous cell carcinoma (SCC) of the oral tongue, as well as validate previously reported predictors of survival. METHODS: We retrospectively reviewed 259 patients treated with curative intent between 1994 and 2004. Kaplan-Meier estimates, log-rank test, and Cox regression models were used for statistical analysis. RESULTS: Two hundred fifty-nine patients were managed with surgery; 67 patients (25%) received adjuvant radiotherapy. Mean follow-up was 60 months. The 5-year local and regional control rates were 78% and 69.4%, respectively. The 5-year overall, disease-specific, and recurrence-free survival rates were 69%, 70.9%, and 53%, respectively. The only significant predictor of both overall survival (OS) and disease-free survival (DFS) on multivariable analysis was pathologic N classification. CONCLUSION: Treatment of early tongue SCC effectively achieves local control and DFS. Nodal disease remains to be 1 of the most important prognostic factors in terms of recurrence and 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)0.000
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 score0.016
Threshold uncertainty score0.286

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 head0.023
GPT teacher head0.309
Teacher spread0.285 · 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

Citations39
Published2012
Admission routes1
Has abstractyes

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