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Record W2932908716 · doi:10.1002/hed.25742

Examined and positive lymph nodes counts and lymph nodes ratio are associated with survival in major salivary gland cancer

2019· article· en· W2932908716 on OpenAlexaff
Khaled Elhusseiny, Fatma Abd‐Elshahed Abd‐Elhay, Mohamed Gomaa Kamel, Heba Hassan Abd El Hamid Hassan, Heba Hussien Muhammad El Tanany, Truong Hong Hieu, Thuan Minh Tieu, Soon Khai Low, Mahmoud Dibas, Nguyen Tien Huy

Bibliographic record

VenueHead & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineLymphInternal medicineEpidemiologyOncologySalivary gland cancerSalivary glandStage (stratigraphy)Parotid glandCancerChemotherapyPathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to investigate the prognostic role of examined (dissected) lymph nodes (ELNs), negative LNs (NLNs), and positive (metastatic) LNs (PLNs) counts and LN ratio (LNR = PLNs/ELNs×100) in patients with major salivary gland cancer (SGC). METHODS: Data were retrieved for major SGC patients diagnosed between 1988 and 2011 from Surveillance, Epidemiology, and End Results program. RESULTS: We have included 5446 patients with major SGC. Most patients had parotid gland cancer (84.61%). Patients having >18 ELNs, >4 PLNs, and >33.33% LNR were associated with a worse survival. Moreover, older age, male patients, grade IV, distant stage, unmarried patients, submandibular gland cancer, and received chemotherapy but not received surgery were significantly associated with a worse survival. CONCLUSIONS: We demonstrated that patients with >18 ELNs and >4 PLNs counts, and >33.33% LNR were high-risk group patients. We strongly suggest adding the ELNs and PLNs counts and/or LNR into the current staging system.

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 score0.007
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.264
Teacher spread0.248 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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