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Record W3113467741 · doi:10.1002/hed.26582

Muscle invasion in oropharyngeal carcinoma undergoing transoral robotic surgery

2020· article· en· W3113467741 on OpenAlexaff
Robert M. McKenzie, Harman S. Parhar, Tony Ng, Eitan Prisman

Bibliographic record

VenueHead & Neck · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLymphovascular invasionMedicinePerineural invasionPathologicalStage (stratigraphy)Surgical marginT-stageCarcinomaTransoral robotic surgeryRetrospective cohort studyBasal cellResection marginInternal medicineOncologyPathologySurgeryOverall survivalMetastasisCancerResectionBiology

Abstract

fetched live from OpenAlex

BACKGROUNDS: Pathologic features of oropharyngeal squamous cell carcinoma (OPSCC) treated with trans-oral robotic surgery predict prognosis and adjuvant therapy. We hypothesized that pathologic muscle invasion (pMI) is associated with poor pathological markers. METHODS: Retrospective review of surgically treated OPSCC to identify pMI and its association with poor pathologic markers. RESULTS: pMI was present in 12/37 patients, and compared to non-pMI, was associated with higher rates of lymphovascular invasion (75% vs. 36%, p = 0.03), perineural invasion (16.7% vs. 0%, p = 0.04), extranodal extension (66.7% vs. 20%, p < 0.01), and tumor stage (8.3% vs. 48% pT1, 75% vs. 52% pT2 and 16.7% vs. 0% pT3). pMI was associated with having a positive margin on main specimen (41.7% vs. 12%, p = 0.04) but not after considering additional margins. CONCLUSIONS: Muscle invasion was associated with higher pathologic tumor staging, poor pathologic factors, and higher rates of positive margin on main specimen.

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 score0.060
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.082
GPT teacher head0.293
Teacher spread0.211 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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