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Record W3087104909 · doi:10.1002/hed.26469

A real‐world comparison of cisplatin vs cetuximab used concurrently with radiation in the treatment of locally advanced oropharyngeal carcinoma

2020· article· en· W3087104909 on OpenAlexaffabout
Andrea S. Fung, Arfan R. Afzal, Robyn Banerjee, Brock Debenham, Desirée Hao

Bibliographic record

VenueHead & Neck · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of AlbertaUniversity of CalgaryAlberta HealthPrincess Margaret Cancer CentreAlberta Health ServicesUniversity Health Network
Fundersnot available
KeywordsMedicineCetuximabInternal medicineProportional hazards modelOncologyStage (stratigraphy)PopulationCisplatinComorbidityHuman papillomavirusRadiation therapyOverall survivalChemotherapyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: This population-based retrospective study compares the efficacy of cisplatin (cis-RT) vs cetuximab (cetux-RT) with concurrent radiation as definitive treatment in patients with oropharyngeal carcinoma (OPC). METHODS: Patients with OPC treated in Alberta with cis-RT or cetux-RT between 2006 and 2016 were evaluated. Median disease-free survival (DFS) and overall survival (OS) were assessed using the Kaplan-Meier method. Multivariable analysis (MVA) was completed with a Cox proportional hazards model. RESULTS: Among 546 patients with OPC, 431 (78.9%) received cis-RT and 115 (21.1%) cetux-RT. Patients treated with cetux-RT were more likely to develop a recurrence after treatment compared to cis-RT (25% vs 15%, P = .01). On MVA, current smoking, human papillomavirus (HPV)-negative status, higher Charlson comorbidity index (CCI), T-stage, and cetux-RT predicted for worse DFS and OS. Outcomes in older patients with a higher CCI still favored cis-RT. CONCLUSIONS: Our data reaffirm results from randomized studies showing better survival outcomes with cis-RT compared to cetux-RT even among those who are >65 with CCI ≥3.

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 score0.041
Threshold uncertainty score0.411

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 head0.052
GPT teacher head0.345
Teacher spread0.294 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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