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Record W3217370305 · doi:10.1002/hed.26943

Achieving negative resection margins in oral cavity cancer with masticator space involvement—Is it feasible? International collaborative study

2021· article· en· W3217370305 on OpenAlexaff
Gilad Horowitz, John R. de Almeida, Ilyes Berania, David P. Goldstein, Hugo Fontan Köhler, Luiz Paulo Kowalski, Leandro Luongo Matos, Adi Brenner, Dan M. Fliss, Nidal Muhanna, Daniel Halpern, Narin N. Carmel Neiderman, Omer J. Ungar, Anton Warshavsky

Bibliographic record

VenueHead & Neck · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineOdds ratioMasticatory forceBasal cellCancerSurgeryInternal medicineDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: Masticator space involvement in oral cavity squamous cell carcinoma (OCSCC) is considered an unresectable disease. Nevertheless, achieving negative resection margins is feasible in limited masticatory space involvement. MATERIALS AND METHODS: A multi-institutional study on OCSCC patients with masticator space invasion who underwent surgical resection. Margin status was assessed according to anatomic tumor involvement of the inframandibular and supra-mandibular notch. RESULTS: One-hundred and thirty-two patients met the inclusion criteria. Then, 67 patients (50.8%) were diagnosed with a supra-notch tumor and 65 (49.2%) with an infra-notch disease. Negative margins were more common in the infra-notch group (43.3 vs. 23.1%, p = 0.014), and positive margins were more common in the supra-notch group (41.5 vs. 23.9%, p = 0.041). Multivariable analysis demonstrated that supra-notch tumors had an increased likelihood for involved resection margins (odds ratio = 2.46, p = 0.036). CONCLUSION: OCSCC patients with masticator space involvement are prone for positive surgical margins in tumors extending above the supra-mandibular notch.

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 score0.094
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.375
Teacher spread0.321 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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