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Record W3204639313 · doi:10.1002/cncr.33936

Longitudinal health utility and symptom‐toxicity trajectories in patients with head and neck cancers

2021· article· en· W3204639313 on OpenAlexaffabout
Jianjun Ren, Wendu Pang, Katrina Hueniken, Ghazal Haddad, Andrew Hope, Shao Hui Huang, Anna Spreafico, Aaron R. Hansen, Bayardo Perez‐Ordoñez, David P. Goldstein, Scott V. Bratman, Wei Zhang, Yu Zhao, Wei Xu, John R. de Almeida, Geoffrey Liu

Bibliographic record

VenueCancer · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentrePublic Health OntarioUniversity of TorontoUniversity Health Network
FundersFundamental Research Funds for the Central UniversitiesChengdu Science and Technology BureauChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMedicineHead and neck cancerRadiation therapyInternal medicineChemoradiotherapyCancerOncologyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: This study examined long-term health utility and symptom-toxicity trajectories among patients with head and neck cancer (HNC). METHODS: For patients diagnosed with HNC (2014-2019), Health Utility Index 3 (HUI-3), Edmonton Symptom Assessment Scale (ESAS), and MD Anderson Symptom Inventory (MDASI) surveys (including both the core and head and neck cancer modules) were prospectively collected at multiple time points (at the baseline, after surgery, during radiotherapy, and 3, 6, 12, and 24 months after treatment). Locally estimated scatterplot smoothing plots were generated to describe HUI-3, ESAS, and MDASI trajectories over time by clinicodemographic factors, treatment modality, and tumor subsite. Contributions of clinical factors were assessed with univariable and multivariable analyses. RESULTS: In 800 patients, the treatment modality and the tumor subsite produced unique HUI-3, ESAS, and MDASI trajectories. Patients treated with surgery alone experienced rapid improvements in HUI-3, ESAS, and MDASI scores postoperatively. Among patients treated with chemoradiotherapy, patients with nasopharyngeal carcinoma had greater declines in HUI-3 during treatment in comparison with patients with oropharyngeal carcinoma, but they had similar ESAS/MDASI scores. Among patients treated with radiotherapy, patients with laryngeal carcinoma had better HUI-3/ESAS/MDASI scores than those with oropharyngeal carcinoma during treatment, but they slowly converged after treatment. Female sex, an age > 75 years, a household income < $40,000, a Charlson comorbidity score > 1, an Eastern Cooperative Oncology Group performance status > 0 (at the baseline), and current smoking were independently associated with worse HUI-3 trajectories. HUI-3 had mild to moderate correlations (ρ = 0.2-0.5) with individual symptom-toxicity trajectories. CONCLUSIONS: Long-term HUI-3 trajectories are associated with tumor subsite, clinicodemographic, and treatment factors, and this may be partly explained by relationships with symptoms/toxicities. Separate evaluations by subsite and treatment should occur in health utility and symptom-toxicity studies of HNC. LAY SUMMARY: This study indicates that the long-term health utility and symptoms/toxicities of patients with the most common head and neck cancers (ie, squamous cell carcinomas and nasopharyngeal carcinomas) differ over time with a variety of factors, including the tumor anatomic site, treatment volume, clinicodemographic characteristics (eg, age, human papillomavirus status, tumor stage, gender, smoking status, alcohol status, education, and comorbidities), and treatment modalities. Generalizations across all head and neck cancers should be strongly discouraged. Future studies should evaluate health utility, symptoms and toxicities, and patient need assessments separately for each anatomic site and treatment modality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.318
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2021
Admission routes2
Has abstractyes

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