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Record W4414085860 · doi:10.1002/hed.70042

Transoral Robotic Surgery ( <scp>TORS</scp> ) Versus Open Surgery for Recurrent Oropharyngeal Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis

2025· article· en· W4414085860 on OpenAlexaff
Francisco Laxague, Drosa Zabihi‐pour, Claudia C. Correa Roa, Josefina Príncipe, Adrian Mendez, Juan M. Fernández Vila, Anthony C. Nichols

Bibliographic record

VenueHead & Neck · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of ManitobaWestern University
Fundersnot available
KeywordsTransoral robotic surgeryOpen surgeryRobotic surgerySquamous cell cancerBasal cellPharynx

Abstract

fetched live from OpenAlex

BACKGROUND: Salvage surgery (SS) is one of the best treatment options for recurrent oropharyngeal squamous cell carcinoma (OPSCC) after prior definitive radiation. METHODS: A Medline literature search of articles on open (OSS) and transoral robotic surgery (TORS) for the treatment of recurrent OPSCC was performed. Surgical, functional, and oncological outcomes were analyzed and compared. RESULTS: A total of 18 studies including 567 (66.2%) patients undergoing OSS and 290 (33.8%) undergoing TORS were included. Patients undergoing TORS vs. OSS had early T- and N-classification tumors, p < 0.01, respectively. OSS patients had higher rates of feeding tube and tracheostomy dependence compared with TORS (p < 0.01). The mean 2-year overall survival in patients undergoing TORS was 68.5% versus 45.9% in the OSS group, p = 0.03. CONCLUSION: Patients undergoing TORS had smaller tumors and less advanced nodal disease. Furthermore, they developed less postoperative feeding tube and tracheostomy dependency, and had better 2-year overall survival than patients undergoing OSS.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.001
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 head0.094
GPT teacher head0.349
Teacher spread0.254 · 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.

Study designMeta-analysis
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

Citations1
Published2025
Admission routes1
Has abstractyes

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