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Record W4294921572 · doi:10.1188/22.onf.409-420

A Mobile Application for Symptom Management in Patients With Breast Cancer

2022· article· en· W4294921572 on OpenAlexaboutno aff
Memnun Seven

Bibliographic record

VenueOncology nursing forum · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerQuality of life (healthcare)Affect (linguistics)CancerInternal medicineOncologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the effect of a symptom management mobile application on quality of life and symptom severity in women with breast cancer undergoing chemotherapy. SAMPLE & SETTING: This parallel randomized pilot study consisted of women with breast cancer admitted to oncology outpatient clinics between November 2019 and January 2021 in Turkey. METHODS & VARIABLES: Participants (N = 40) were randomly assigned to the intervention (n = 20) or control group (n = 20). The intervention group used the mobile application in conjunction with usual care. The control group received usual care. Participants were assessed during the first, third, and last chemotherapy cycles. Data were collected using the European Organisation for Research and Treatment of Cancer Quality-of-Life Questionnaire-Core 30 and the Edmonton Symptom Assessment System. RESULTS: During the study, the decrease in general health and physical functioning and the increase in the severity of depression/sadness in the intervention group were statistically lower than in the control group. IMPLICATIONS FOR NURSING: The use of a mobile application for symptom management may promote general well-being and physical function and may alleviate symptoms of depression/sadness in women with breast cancer undergoing chemotherapy. Further studies are needed to evaluate the application in clinical settings with larger groups.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.424

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 head0.006
GPT teacher head0.295
Teacher spread0.289 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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