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Record W2016037145 · doi:10.1002/hed.24042

Role of primary surgery in the treatment of advanced oropharyngeal cancer

2015· article· en· W2016037145 on OpenAlexaff
Hadi Seikaly, Vincent L. Biron, Han Zhang, Daniel A. O’Connell, David W. J. Côté, Khalid Ansari, Lakshmi Puttagunta, Jeffrey Harris

Bibliographic record

VenueHead & Neck · 2015
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsMedicineChemoradiotherapyInternal medicineOncologyMultivariate analysisStage (stratigraphy)CancerHead and neck cancerAdjuvantProportional hazards modelPopulationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to compare survival outcomes of patients with advanced stage oropharyngeal squamous cell carcinoma (SCC) according to surgical and nonsurgical treatments, when stratified by smoking and p16 status. METHODS: We conducted a prospective longitudinal population-based study of 279 patients diagnosed with advanced oropharyngeal SCC. Patients were stratified by smoking and p16 status. RESULTS: There was no significant disease-specific survival (DSS) difference in combined modality treatment groups in nonsmokers that had p16-positive cancers, however, in smokers with p16-positive cancers, the DSS surgery + postoperative chemoradiotherapy (S+CRT) was significantly higher than chemoradiotherapy (CRT) alone. Patients who had p16-negative cancers had the highest DSS when treated with surgery + adjuvant therapy (S+RT)/CRT. Multivariate Cox regression analysis showed that increasing Eastern Cooperative Oncology Group (ECOG) score, smoking, p16 status, higher stage, and treatment with surgery protocols were significant determinants of survival. CONCLUSION: Primary surgical approaches offer the best survival outcomes in smokers with p16-positive cancers and in patients with p16-negative cancers. © 2015 Wiley Periodicals, Inc. Head Neck 38: E-E, 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.056
GPT teacher head0.331
Teacher spread0.274 · 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 designObservational
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

Citations38
Published2015
Admission routes1
Has abstractyes

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