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Record W3132543689 · doi:10.1002/hed.26657

Novel imaging classification system of nodal disease in human papillomavirus‐mediated oropharyngeal squamous cell carcinoma prognostic of patient outcomes

2021· article· en· W3132543689 on OpenAlexaff
Farahna Sabiq, Kitty Huang, Adarsh Patel, Robyn Banerjee, Brock Debenham, Harold Lau, David Skarsgard, Guanmin Chen, John T. Lysack, Harvey Quon

Bibliographic record

VenueHead & Neck · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsLymphMedicineHuman papillomavirusOncologyBasal cellCervical lymph nodesDiseaseRadiologyInternal medicinePathologyCancerMetastasis

Abstract

fetched live from OpenAlex

BACKGROUND: Matted nodes in human papillomavirus (HPV)-mediated oropharyngeal squamous cell carcinoma (OPC) is an independent predictor of distant metastases and decreased overall survival. We aimed to classify imaging patterns of metastatic lymphadenopathy, analyze our classification system for reproducibility, and assess its prognostic value. METHODS: The metastatic lymphadenopathy was classified based on radiological characteristics for 216 patients with HPV-mediated OPC. Patient outcomes were compared and inter-rater reliability was calculated. RESULTS: The presence of ≥3 abutting lymph nodes with imaging features of surrounding extranodal extension (ENE), one subtype of matted nodes, was associated with worse 5-year overall survival, overall recurrence-free survival, regional recurrence-free survival, and distant recurrence-free survival (p ≤ 0.03). Other patterns were not significantly associated with outcome measures. Overall inter-rater agreement was substantial (κ = 0.73). CONCLUSION: One subtype of matted nodes defined by ≥3 abutting lymph nodes with imaging features of surrounding ENE is the radiological marker of worst prognosis.

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 score0.077
Threshold uncertainty score0.656

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 head0.026
GPT teacher head0.286
Teacher spread0.261 · 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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