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Record W4200137190 · doi:10.7759/cureus.20255

Prevalence of Metastasis and Involvement of Level IV and V in Oral Squamous Cell Carcinoma: A Systematic Review

2021· review· en· W4200137190 on OpenAlexaboutno aff
Ahmad Altuwaijri, Turki Aldrees, Mohammed Alessa

Bibliographic record

VenueCureus · 2021
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOccultNeck dissectionMetastasisIncidence (geometry)Basal cellCancerOncologyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

The occurrence of occult metastases in oral cavity squamous cell carcinoma (OSCC) to lower levels in the neck (levels IV and V) or development of skip metastases that bypass the upper neck levels (levels I to III) and go directly to level IV or V is common. This challenges the efficacy of conventional neck dissection approaches in the treatment of OSCC. Therefore, the decision to include lower levels cervical nodes during elective neck dissection of OSCC remains controversial. This systematic review was designed to assess the prevalence of level IV and/or V involvement or skip metastases in patients with the clinically negative neck (cN0) or positive (cN+) oral squamous cell carcinoma (OSCC). We searched for studies published between December 2000 and December 2020. Potentially relevant abstracts and full-text articles were screened, and data from the studies were extracted. Quality was rated using the Newcastle Ottawa Scale (NOS) criteria. In total, 802 abstracts and 227 full-text articles were screened, and 32 studies were included in this analysis. The prevalence of metastasis ranged from 1.8% to 66.0%. The incidence for skip metastasis to level IV or V was low, reaching 8.5%. Evidence favored elective neck dissection, including levels I to III, in selected patients with OSCC and patients with cN0 or cN+ neck. The literature was non-conclusive on the recommendation for inclusion of lower levels.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
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 head0.132
GPT teacher head0.366
Teacher spread0.234 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2021
Admission routes1
Has abstractyes

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