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Record W1966000261 · doi:10.2310/7070.2005.4133

Involvement of Level I Neck Lymph Nodes and Submandibular Gland in Laryngeal and/or Hypopharyngeal Squamous Cell Carcinoma

2006· article· en· W1966000261 on OpenAlexvenueno aff
Giuseppe Mercante, Andrea Bacciu, Gabriele Oretti, Teore Ferri

Bibliographic record

VenueThe Journal of Otolaryngology · 2006
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeck dissectionSubmandibular glandLarynxStage (stratigraphy)CarcinomaHypopharyngeal cancerLymph nodeAccessory nerveDissection (medical)PathologySurgeryRadiation therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the prevalence of level I neck lymph node metastases or submandibular nodal metastases in laryngeal and/or hypopharyngeal squamous cell carcinoma (SCC). PATIENTS AND METHODS: One hundred fifty consecutive neck dissection specimens from 100 patients with laryngeal and hypopharyngeal SCC, who were treated at our institution between 1992 and 2002, were retrospectively reviewed. RESULTS: The tumour stage was T1-T4, and the neck stage was N0-N3. Metastases were never found in level I (Ia + Ib) or in the submandibular gland. Metastases were concentrated within the jugular chain (levels II-IV in 92.2% of the N-positive necks). CONCLUSION: Metastases of level I of the neck and the submandibular gland are extremely rare in cases of laryngeal and/or hypopharyngeal carcinoma. The risk of facial or hypoglossal nerve injury does not justify the dissection of level I and of the submandibular gland in this type of tumour.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.261
Teacher spread0.235 · 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 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

Citations11
Published2006
Admission routes1
Has abstractyes

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