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Record W1516602561 · doi:10.1002/hed.24120

Prospective study of sentinel node biopsy for high‐risk cutaneous squamous cell carcinoma of the head and neck

2015· article· en· W1516602561 on OpenAlexaff
Sinclair Gore, Douglas V. Shaw, Richard C. Martin, Wendy Kelder, Kathryn Roth, Roger F. Uren, Kan Gao, Sarah Davies, Bruce Ashford, Quan Ngo, Kerwin F. Shannon, Jonathan R. Clark

Bibliographic record

VenueHead & Neck · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsSt Joseph's Health Care
Fundersnot available
KeywordsMedicinePerineural invasionLymphovascular invasionBiopsySentinel lymph nodeSentinel nodeMetastasisOncologyBasal cellInternal medicineHead and neckSurgeryCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Nodal metastasis from cutaneous squamous cell carcinoma (SCC) is poorly predicted clinically and is associated with a high mortality rate. METHODS: From 2010 to 2013, patients with high-risk cutaneous SCC were assessed with sentinel node biopsy (SNB) either at the time of primary cutaneous tumor resection or at secondary wide local excision. RESULTS: Of 57 patients, 8 (14%) had nodal metastasis. Significant predictors of metastasis are the number of high-risk factors (p = .008), perineural invasion (PNI; p = .05), and lymphovascular invasion (LVI; p = .05). During a mean of 19.4 months, 9 patients developed recurrence and 6 died of cutaneous SCC, indicating that over 1300 patients would be required for a randomized controlled trial with 80% power to detect a significant difference in disease-free survival. CONCLUSION: Lymph node metastasis occurs in 14% of patients with high-risk cutaneous SCC. Larger studies will be required to identify which "high-risk" factors should be considered as an indication for surgical assessment of the nodal basin. © 2015 Wiley Periodicals, Inc. Head Neck 38: E884-E889, 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 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)0.004
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.282
Teacher spread0.258 · 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

Citations90
Published2015
Admission routes1
Has abstractyes

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