MétaCan
Menu
Back to cohort
Record W2773643265 · doi:10.1097/spc.0000000000000323

The fear of cancer recurrence literature continues to move forward: a review article

2017· review· en· W2773643265 on OpenAlexaff
Christine Maheu, Jacqueline Galica

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2017
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychological interventionMedicineMEDLINEIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The volume of literature addressing fear of cancer recurrence (FCR) is rapidly increasing. A summary of key developments in the research and treatment of FCR was published by Sharpe et al. in 2017, and the current review focuses on works published thereafter. RECENT FINDINGS: A comprehensive literature review was conducted to provide an up-to-date summary of peer-reviewed publications focusing on FCR. The search consisted of the most recent FCR reports published between 2016 and 2017, which can be broadly categorized as: methods of assessment; associations with FCR; FCR and caregivers; and FCR interventions. SUMMARY: FCR assessments continue to undergo revisions, which may have positive implications for clinicians and researchers seeking shorter measures to assess the FCR of their patients and study participants. However, research is needed to determine if a shorter FCR measure could be created using items that measure the construct alone, yet still retaining optimal sensitivity and specificity, or also with its determinants and consequences. Doing so would result in either unidimensional or multidimensional measure of FCR. Notwithstanding these matters in FCR assessment, the state of the literature continues to advance our understanding about characteristics of survivors with highest FCR, which is useful to identify patients in need of FCR interventions. These empirical results are useful to further investigate the theoretical distinctions of FCR.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.148
GPT teacher head0.491
Teacher spread0.343 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Supportive and Palliative CareSame topicCancer survivorship and careFrench-language works237,207