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Record W1969775436 · doi:10.3747/co.2007.159

The Anti-tumor Activities of Statins

2007· article· en· W1969775436 on OpenAlexvenueno aff
Masahiro Nishibori

Bibliographic record

VenueCurrent Oncology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsMedicineBioinformaticsBiology

Abstract

fetched live from OpenAlex

Home-based informal caregivers (CGs), such as the family members and friends of cancer patients, often suffer averse emotional symptoms, such as anxiety and depression, due to the burden associated with providing care.The natural environment has been valued as a healing sanctuary for easing emotional pain, promoting calmness, relaxation, and restoration.The use of virtual reality (VR) nature experiences offers an alternative option to CGs to manage emotional symptoms and improve their quality of life.The aim of this mixed-method pilot was to evaluate the feasibility and acceptability of a nature-based VR experience for home-based CGs.Nine informal CGs participated in a 10 min nature-based VR session and completed feasibility, acceptability, and VR symptom measures in the laboratory.Semi-structured interviews with five of the CGs provided qualitative data regarding their experiences with VR.The CGs (mean age 64.78 years) were mostly female (n = 7).Our analysis showed high feasibility (15.11 ± 1.76; range 0-16) and acceptability (15.44 ± 1.33; range 0-16), as well as low VR Symptoms (1.56 ± 1.33; range 0-27).Participants primarily expressed positive perceptions regarding VR feasibility and acceptability during interviews.Our findings show promise for the use of VR nature experiences.In the next phase of the study, the intervention will be tested on home-based informal CGs of patients at end of life.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.266

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 head0.036
GPT teacher head0.369
Teacher spread0.333 · 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 designNot applicable
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

Citations12
Published2007
Admission routes1
Has abstractyes

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