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Record W2599401193 · doi:10.1002/hed.24760

Body mass index and prognosis in patients with head and neck cancer

2017· article· en· W2599401193 on OpenAlexaffabout
Ricardo Ribeiro Gama, Yuyao Song, Qihuang Zhang, M. Catherine Brown, Jennifer Wang, Steven Habbous, Tong Li, Shao Hui Huang, Brian O’Sullivan, John Waldron, Wei Xu, David P. Goldstein, Geoffrey Liu

Bibliographic record

VenueHead & Neck · 2017
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineUnderweightBody mass indexOverweightHazard ratioHead and neck cancerInternal medicineCancerConfidence intervalOncology

Abstract

fetched live from OpenAlex

BACKGROUND: Body mass index (BMI) has been associated variably with head and neck cancer outcomes. We evaluated the association between BMI at either diagnosis or at early adulthood head and neck cancer outcomes. METHODS: Patients with invasive head and neck squamous cell cancer at Princess Margaret Cancer Centre in Toronto, Canada, were surveyed on tobacco and alcohol exposure, performance status, comorbidities, and BMI at diagnosis. A subset also had data collected for BMI at early adulthood. RESULTS: With a median follow-up of 2.5 years, in 1279 analyzed patients, being overweight (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.4-0.8; p = .001) at diagnosis was associated with improved survival when compared with individuals with normal weight. In contrast, underweight patients at diagnosis were associated with a worse outcome (HR, 1.89; 95% CI, 1.2-3.1; p < .01). CONCLUSION: Being underweight at diagnosis was an independent, adverse prognostic factor, whereas being overweight conferred better prognosis. BMI in early adulthood was not associated strongly with head and neck cancer outcomes. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1226-1233, 2017.

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 score0.012
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.310
Teacher spread0.288 · 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

Citations89
Published2017
Admission routes2
Has abstractyes

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