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Record W2108463286 · doi:10.1002/hed.24059

Decision management in transoral robotic surgery: Indications, individual patient selection, and role in the multidisciplinary treatment for head and neck cancer from a European perspective

2015· review· en· W2108463286 on OpenAlexaff
Balázs B. Lőrincz, Nate Jowett, Rainald Knecht

Bibliographic record

VenueHead & Neck · 2015
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransoral robotic surgeryTransoral laser microsurgeryMedicineHead and neck cancerHead and neck squamous-cell carcinomaQuality of life (healthcare)Multidisciplinary approachCancerSurgeryOncologyGeneral surgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Transoral robotic surgery (TORS) has become an accepted first-line treatment for T1 and T2 head and neck squamous cell carcinoma (HNSCC). The growing popularity of this procedure is the result of mounting skepticism as to the survival and quality of life (QOL) benefits of primary chemoradiation over definitive surgery, the rising incidence of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) in progressively younger patients, and the advantages of TORS over transoral laser microsurgery (TOLM) and open surgery. METHODS: The authors use their experience and data gained from the TORS-based management of >100 patients to establish a systematic approach to the use of TORS in HNSCC. RESULTS: This approach is constructed on a framework which goal is to select the primary treatment option that is most likely to reduce morbidity while preserving function and maintaining oncologic safety. CONCLUSION: A consensus regarding the indications of TORS and its role in the multidisciplinary management of HNSCC is to be established. © 2015 Wiley Periodicals, Inc. Head Neck 38: E2190-E2196, 2016.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.100
GPT teacher head0.397
Teacher spread0.297 · 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 designOther design
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

Citations58
Published2015
Admission routes1
Has abstractyes

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