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Record W4402384993 · doi:10.1177/19160216241278653

Tumor Bed Margins Versus Specimen Margins in Oral Cavity Cancer: Too Close to Call?

2024· article· en· W4402384993 on OpenAlexafffund
Noémie Villemure‐Poliquin, Ève-Marie Roy, Sally Nguyen, Michel Beauchemin, Nathalie Audet

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill UniversityCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversité Laval
FundersCentre Hospitalier Universitaire de Québec
KeywordsMedicineOral cavitySampling (signal processing)Margin (machine learning)CancerSurgeryStandard of careOral CancersRadiologyDentistryInternal medicineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Introduction The routine assessment of intraoperative margins has long been the standard of care for oral cavity cancers. However, there is a controversy surrounding the best method for sampling surgical margins. The aim of our study is to determine the precision of a new technique for sampling tumor bed margins (TBMs), to evaluate the impact on survival and the rate of free flap reconstructions. Methods This retrospective cohort study involved 156 patients with primary cancer of the tongue or floor of the mouth who underwent surgery as initial curative treatment. Patients were separated into 2 groups: one using an oriented TBM derived from Mohs’ technique, where the margins are taken from the tumor bed and identified with Vicryl sutures on both the specimen and the tumor bed, and the other using a specimen margins (SMs) driven technique, where the margins are taken from the specimen after the initial resection. Clinicopathologic features, including margin status, were compared for both groups and correlated with locoregional control. Precision of per-operative TBM sampling method was obtained. Results A total of 156 patients were included in the study, of which 80 were in TBM group and 76 were in SM group. Precision analysis showed that the oriented TBM technique pertained a 50% sensitivity, 96.6% specificity, 80% positive predictive value, and an 87.5% negative predictive value. Survival analysis revealed nonstatistically significant differences in both local control (86.88% vs 83.50%; P = .81) as well as local-regional control (82.57% vs 72.32%; P = .21). There was a significant difference in the rate of free flap-surgeries between the 2 groups (30% vs 64.5%; P < .001). Conclusion Our described oriented TBM technique has demonstrated reduced risk of free flap reconstructive surgery, increased precision, and similar prognostic in terms of local control, locoregional control, and disease-free survival when compared to the SM method.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.327
Teacher spread0.291 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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