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Record W2801191315 · doi:10.1002/pon.4748

Utilization of health care services in cancer patients with elevated fear of cancer recurrence

2018· article· en· W2801191315 on OpenAlexafffund
Alexandra Champagne, Hans Ivers, Josée Savard

Bibliographic record

VenuePsycho-Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsPsychosocialMedicineCancerInsomniaPsychiatryCancer recurrenceDepression (economics)Health professionalsHealth careFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer patients commonly report experiencing fear of cancer recurrence (FCR), which may lead to several negative consequences. This study aimed at examining whether clinical levels of FCR are linked to a greater use of health care services. METHOD: This is a secondary analysis of a longitudinal study of 962 cancer patients on the epidemiology of cancer-related insomnia. They completed the Fear of Cancer Recurrence Inventory-Short form (FCRI-SF) and reported information on their consultations (medical, psychosocial, and complementary and alternative medicine [CAM]) and medication usage (anxiolytics/hypnotics and antidepressants) at 6 time points over an 18-month period. RESULTS: Results indicated that clinical FCR at baseline was associated with greater consultation rates of medical and psychosocial professionals and a greater usage of anxiolytics/hypnotics and antidepressants. No significant association was found between the FCR level and use of CAM services. While consultation rates of medical and CAM professionals and usage of antidepressants generally increased over time, consultation rates of psychosocial professionals and usage of anxiolytics/hypnotics tended to decrease. CONCLUSIONS: Cancer patients with clinical levels of FCR are more likely to consult health care providers and to use psychotropic medications, which may translate into significant costs for society and the patients themselves.

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 score0.150
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.396
Teacher spread0.363 · 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

Citations58
Published2018
Admission routes2
Has abstractyes

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