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Record W4312212178 · doi:10.1002/cncy.22678

The 2021 ASCO guideline on the management of salivary gland malignancy endorses FNA biopsy and the risk stratification scheme proposed by the Milan System for Reporting Salivary Gland Cytopathology

2022· article· en· W4312212178 on OpenAlexaff
Marc Pusztaszeri, Daniel G. Deschler, William C. Faquin

Bibliographic record

VenueCancer Cytopathology · 2022
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineGuidelineCytopathologySalivary glandFine-needle aspirationBiopsySalivary gland cancerMalignancyPathologyCytology

Abstract

fetched live from OpenAlex

The 2021 American Society of Clinical Oncology (ASCO) guideline for the management of salivary gland cancer offers 47 evidence‐based recommendations on preoperative evaluations, diagnostic and therapeutic surgical procedures, radiotherapy, systemic therapy, follow‐up evaluations, and recurrent metastatic disease. The ASCO guideline provides a framework and the best current evidence for managing the care of patients with salivary gland malignancies from diagnosis to treatment, and it endorses fine‐needle aspiration biopsy and the new Milan System for Reporting Salivary Gland Cytopathology.

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)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.008

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 head0.297
Teacher spread0.273 · 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 designNot applicable
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

Citations18
Published2022
Admission routes1
Has abstractyes

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