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Record W4224264391 · doi:10.5737/23688076322214222

Exploring the effect of neurofeedback on postcancer cognitive impairment and fatigue: A pilot feasibility study

2022· article· en· W4224264391 on OpenAlexafffundvenueabout
Marian Luctkar‐Flude, Jane Tyerman, Shawna Burnett, Janet Giroux, Dianne Groll

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsKingston Health Sciences CentreUniversity of OttawaQueen's University
FundersQueen's University
KeywordsNeurofeedbackPsychosocialQuality of life (healthcare)Physical therapyCognitionMedicineBreast cancerMoodPhysical medicine and rehabilitationPsychologyClinical psychologyPsychiatryCancerElectroencephalographyInternal medicine

Abstract

fetched live from OpenAlex

Purpose: Postcancer cognitive impairment (PCCI) and fatigue are adverse effects that often persist following cancer treatment, and impact quality of life. The study purpose was to evaluate feasibility and effect of neurofeedback on cognitive functioning and fatigue in cancer survivors. Specifically, we aimed to test feasibility of recruitment strategies and our study protocol including outcome measures. Design: This pilot feasibility study used a 10-week wait-list design. Participants served as their own controls and received neurofeedback training twice a week for 10 weeks. Participants: The sample consisted of breast cancer survivors from Kingston, Ontario (n = 16). Methods: Outcomes were assessed using validated, self-report scales and neuropsychological tests before, during, and after neurofeedback. Findings: The neurofeedback protocol was feasible and resulted in significant decreases in perceived cognitive deficits, fatigue, sleep, and psychological symptoms. Implications for psychosocial providers: Neurofeedback may be an effective, non-invasive complementary therapy for PCCI in breast cancer survivors.

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)0.001
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 score0.239
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.114
GPT teacher head0.372
Teacher spread0.258 · 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

Citations1
Published2022
Admission routes4
Has abstractyes

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