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Record W3195855446 · doi:10.1002/hed.26836

Outcomes for the treatment of locoregional recurrent nasopharyngeal cancer: Systematic review and pooled analysis

2021· review· en· W3195855446 on OpenAlexaff
Ethan Newton, Dianne Valenzuela, J Foley, Andrew Thamboo, Eitan Prisman

Bibliographic record

VenueHead & Neck · 2021
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineNasopharyngeal carcinomaBrachytherapyRadiation therapyRadiosurgerySurgeryPooled analysisCancerNasopharyngeal cancerOncologyInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

Abstract Despite advances in the treatment of primary nasopharyngeal carcinoma, locoregional recurrence (lrNPC) occurs at 10%–50% at 5 years. This review aims to evaluate salvage treatment for locally recurrent nasopharyngeal cancer. A literature search for all original articles published on the treatment of lrNPC from January 1990 to January 2021 was conducted. Pooled analysis was performed using a random effects model and assessed statistical heterogeneity of the combined results with I2 index. Overall, 66 studies were included for analysis. A total of 5286 patients treated with intensity‐modulated radiation therapy (39%), conformal radiotherapy (31%), open nasopharyngectomy (12%), endoscopic nasopharyngectomy (10%), stereotactic radiosurgery (4%), and brachytherapy (4%) were included. Surgical therapy has similar overall survival outcomes to re‐irradiation but with decreased treatment‐related morbidity and mortality. Both surgical and re‐irradiation for lrNPC have similar long‐term survival. Surgical approaches to lrNPC may offer similar survival while avoiding treatment‐associated morbidity and mortality.

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.017
Bibliometrics0.0080.009
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.098
GPT teacher head0.424
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations18
Published2021
Admission routes1
Has abstractyes

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