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Record W4411495036 · doi:10.1002/hed.28219

Current Trends in the Management of Recurrent Nasopharyngeal Carcinoma

2025· review· en· W4411495036 on OpenAlexaff
Vivek C. Pandrangi, Jay J. Liao, John R. de Almeida, Ivan H. El‐Sayed, Glenn J. Hanna, Shirley Y. Su, Raymond K. Tsang, Tae‐Bin Won, Ian Witterick, Garret Choby, Edward C. Kuan, Mathew Geltzeiler

Bibliographic record

VenueHead & Neck · 2025
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNasopharyngeal carcinomaRadiation therapyNarrative reviewChemotherapyModalitiesIntensive care medicineOncologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Recurrent nasopharyngeal carcinoma (NPC) is associated with challenges in treatment due to the complex anatomic location and impact of prior treatment modalities such as radiation therapy. The purpose of this review is to discuss modern treatment strategies for recurrent NPC, potential challenges, and outcomes. METHODS: A narrative review was performed, evaluating management strategies of recurrent NPC, survival measures, and advancements in treatment considerations. RESULTS: Treatment options including radiation, surgery, and chemotherapy are discussed, including data on survival outcomes and treatment-related morbidity. We review additional considerations including advances in endoscopic surgery, operative management of the internal carotid artery (ICA), novel radiation and chemotherapy protocols, and the introduction of immune checkpoint inhibitors. CONCLUSION: This review describes contemporary management strategies for recurrent NPC, highlighting evolving management strategies that may reduce treatment-associated morbidity and improve survival.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.411
Teacher spread0.326 · 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 designNot applicable
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

Citations3
Published2025
Admission routes1
Has abstractyes

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