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Record W2923750060 · doi:10.1002/hed.25725

Contextual and historical factors for increased levels of anxiety and depression in patients with head and neck cancer: A prospective longitudinal study

2019· article· en· W2923750060 on OpenAlexaff
Mélissa Henry, Fabienne Fuehrmann, Michael Hier, Anthony Zeitouni, Karen Kost, Keith Richardson, Alex Mlynarek, Martin J. Black, Christina MacDonald, Gabrielle Chartier, Xun Zhang, Zeev Rosberger, Saul Frenkiel

Bibliographic record

VenueHead & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsAnxietyNeuroticismDepression (economics)Longitudinal studyClinical psychologyPsychiatryProspective cohort studyMedicineHead and neck cancerPsychologyCancerInternal medicinePersonality

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed at examining predictors of clinical anxiety and depressive symptoms in patients with head and neck cancer (HNC) at 3, 6, and 12 months post-diagnosis, with a particular interest in contextual and historical factors. METHODS: Prospective longitudinal study of 219 consecutive patients newly diagnosed with a first occurrence of primary HNC, including psychometric measures, Structured Clinical Interview for DSM-IV Diagnoses (SCID), and medical chart reviews. RESULTS: Point prevalence of clinical anxiety symptoms (Hospital Anxiety and Depression Scale-Anxiety subscale) was 32.0%, 21.9%, 12.1%, and 12.6% at baseline, 3, 6, and 12 months; and clinical depressive symptoms on the Depression Subscale was 19.4%, 21.9%, 13.5%, and 9.2%, respectively. Predictors of anxiety and depressive symptoms included upon diagnosis SCID major depressive or anxiety disorder, stressful life events in previous year, neuroticism, and levels of anxiety and depressive symptoms upon cancer diagnosis. CONCLUSIONS: This study emphasizes the predictive contribution of broader personal contextual and historical factors that increase psychological vulnerability in HNC and merit consideration.

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 score0.009
Threshold uncertainty score0.996

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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