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Record W1967408801 · doi:10.1002/hed.20924

Prognostic factors in malignancy of the minor salivary glands

2008· article· en· W1967408801 on OpenAlexaff
Kwok Seng Loh, Emma Barker, G. Bruch, Brian O’Sullivan, Dale H. Brown, David P. Goldstein, Ralph Gilbert, Patrick Gullane, Jonathan C. Irish

Bibliographic record

VenueHead & Neck · 2008
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineAdenoid cystic carcinomaMucoepidermoid carcinomaMalignancyMinor Salivary GlandsPathologicalInternal medicineSalivary glandMultivariate analysisOncologyRadiation therapySalivary gland cancerHead and neckGastroenterologyAdenoidCarcinomaSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to identify prognostic factors associated with minor salivary gland malignancies. METHOD: This was a retrospective study of 171 patients. Statistical analysis of both clinical and pathological parameters with survival outcomes was performed. RESULTS: Adenoid cystic carcinoma (46.8%), mucoepidermoid carcinoma (22.8%), and adenocarcinoma (18.7%) were the most common pathologies. The most frequent sites of primary tumor were in the oral cavity (44.4%) and nasal cavity (40.4%). The 5-year and 10-year overall survivals were 73.8% and 58.1%. Disease-specific survivals (DSS) were 78.2% and 66.7% and disease-free survivals (DFS) were 64.8% and 47.5%, respectively. Local recurrence rate was 26.9%, regional recurrence 7%, and distant failure was 18.7%. The grade of tumor was the only factor associated with DSS on multivariate analysis. CONCLUSION: Overall and DSS of minor salivary gland malignancies were good. Surgery, either alone or in combination with radiation, was an efficacious treatment modality. High-grade tumors were associated with worse DSF.

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 score0.005
Threshold uncertainty score0.300

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 head0.039
GPT teacher head0.272
Teacher spread0.233 · 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

Citations54
Published2008
Admission routes1
Has abstractyes

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