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Record W2291953387 · doi:10.1002/hed.24302

Impact of elective neck dissection on the outcome of oral squamous cell carcinomas arising in the maxillary alveolus and hard palate

2015· article· en· W2291953387 on OpenAlexaff
Babak Givi, Antoine Eskander, Mahmoud Awad, Qin Kong, Pablo H. Montero, Frank L. Palmer, Wei Xu, John R. de Almeida, Nancy Y. Lee, Brian O’Sullivan, Jonathan C. Irish, Ralph Gilbert, Ian Ganly, Snehal G. Patel, David P. Goldstein, Luc G.T. Morris

Bibliographic record

VenueHead & Neck · 2015
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Institute of Dental and Craniofacial ResearchNational Cancer InstituteNational Institutes of Health
KeywordsMedicineNeck dissectionHard palateOccultSurgeryDissection (medical)Lymph nodeCarcinomaInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Whether elective lymph neck dissection (ELND) is associated with improved survival in oral squamous cell carcinomas (SCC) of the maxillary alveolus/hard palate is not known. METHODS: One hundred ninety-nine patients presenting de novo and receiving treatment for clinically node negative SCC of the maxillary alveolus/hard palate at 2 cancer centers between 1985 and 2011 were analyzed. RESULTS: Forty-two patients (21%) received ELND. Occult nodal metastases were present in 29% of the dissected necks. The ELND group had more T3 to T4 status tumors (62% vs 34%; p < .001) and positive-margin resections (59% vs 38%; p = .019). Patients undergoing ELND experienced lower rates of neck recurrence (6% vs 21%; p = .031), superior 5-year recurrence-free survival (68% vs 45%; p = .026), and overall survival (86% vs 62%; p = .043). ELND was associated with a 2-fold decrease in risk of recurrence in multivariable analysis. CONCLUSION: ELND was associated with lower rates of recurrence and improved survival in SCC of the maxillary alveolus/hard palate. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1688-E1694, 2016.

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 score0.006
Threshold uncertainty score0.503

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 head0.073
GPT teacher head0.348
Teacher spread0.275 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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