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Prognostic factors in the surgical treatment of patients with oral carcinoma

2009· article· en· W2014926717 on OpenAlexaff
Rajan S. Patel, Jonathan R. Clark, Richard Dirven, Rebecca Wyten, Kan Gao, Christopher J. O’Brien

Bibliographic record

VenueANZ Journal of Surgery · 2009
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineNeck dissectionCarcinomaOral cavityStage (stratigraphy)Retrospective cohort studySurgeryHead and neck cancerPathologicalHead and neckCohortDissection (medical)CancerDiseaseOverall survivalInternal medicineDentistry

Abstract

fetched live from OpenAlex

The aim of the study was to analyse the clinical outcome of patients treated surgically for oral carcinoma. A retrospective cohort study was undertaken of 356 patients with oral cavity cancer whose clinicopathological information had been collected prospectively onto a dedicated head and neck database. Disease recurrence and survival were assessed. Neck metastases occurred in 42% of patients. Tumour thickness (both 2 and 5 mm) predicted the presence of nodal metastases. Both pathological T stage (P < 0.001) and tumour thickness cut-off of 5 mm (P = 0.03) were independent predictors of disease-specific survival. With a median follow up of 41 months, overall survival at 5 years was 59% and disease-specific survival was 73%. Patients with thick tumours have a high risk of nodal metastases and this supports the liberal use of elective selective neck dissection in patients with clinically negative necks.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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

Citations75
Published2009
Admission routes1
Has abstractyes

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