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Record W4220736338 · doi:10.1111/ecc.13581

Routine follow‐up care for head and neck cancer after curative treatment: A 3‐year experience of measuring patients' self‐reported needs, preferences, quality of life and attitudes towards follow‐up

2022· article· en· W4220736338 on OpenAlexaff
Kelly Brennan, Stephen F. Hall, John Yoo, Susan L. Rohland, Julie Theurer, Paul Peng

Bibliographic record

VenueEuropean Journal of Cancer Care · 2022
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsLawson Health Research InstituteWestern UniversityQueen's University
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Head and neck cancerDiseaseHead and neckExploratory researchCancerPhysical therapyStage (stratigraphy)Family medicineInternal medicineSurgeryNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate and describe attitudes, quality of life (Qol), needs and preferences of patients with head and neck cancer after 3 years of follow-up care. METHODS: This is an exploratory prospective study of recurrence-free patients. Survey results were compared between 1-, 2- and 3-year post-treatment and by disease characteristics. RESULTS: A total of 116 patients were included with 46% oropharyngeal cancer, 66% early stage disease and 41% having had surgery. After 3 years, most patients reported good to excellent health (88%), however expressed uncertainty regarding recurrence (66%), multiple needs (information on prognosis 91%, long-term sequalae 72%) and wanted to continue with follow-up (96%). Few changes were observed over time, with exceptions. Patients with more advanced disease, oral cancer or who had surgery experienced declining Qol (p < 0.050). Women experienced improvements in Qol domains (pain p = 0.028, speech p = 0.009) over time. Attitudes towards communication with oncologists demonstrated improved patient comfort (p = 0.044) over the 3 years; however, patients' beliefs about their prognosis did not (71% vs. 73% vs. 77% did not believe they were cured, p = 0.581). CONCLUSION: Although patients' needs, preferences and attitudes towards follow-up did not change drastically, important needs persist. This work supports identifying individual patient needs and the challenges in addressing prognostic expectations.

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 score0.114
Threshold uncertainty score0.783

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 head0.078
GPT teacher head0.348
Teacher spread0.270 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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