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Record W2810425232 · doi:10.1002/pbc.27278

Evaluation of mobile phone applications to support medication adherence and symptom management in oncology patients

2018· review· en· W2810425232 on OpenAlexafffund
Jennifer Jupp, Humirah Sultani, Cassandra A. Cooper, Kedra A. Peterson, Tony H. Truong

Bibliographic record

VenuePediatric Blood & Cancer · 2018
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of CalgaryStollery Children's HospitalUniversity of AlbertaAlberta HealthAlberta Children's HospitalAlberta Health Services
FundersUniversity of AlbertaAlberta Health Services
KeywordsMedicineMobile phoneMobile appsPediatric oncologyRating scaleDisease managementSmartphone appPhoneMEDLINEInternal medicineOncologyDiseaseWorld Wide WebCancer

Abstract

fetched live from OpenAlex

Mobile phone applications (apps), may support pediatric oncology patients with medication and disease management. A scoping review of the literature, a search of the iTunes App and Google Play Stores, was conducted to identify medication and symptom management apps for adult and pediatric oncology patients. Pooled results yielded 28 apps which were assessed for quality using the Mobile Application Rating Scale, with mean overall scores ranging from 2.8 to 4.3. Most apps received low scores in the Engagement domain. Our study assessed the quality of available mobile oncology apps and identified areas for improvement in design and function.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.528
Teacher spread0.409 · 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.

Study designNot applicable
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

Citations67
Published2018
Admission routes2
Has abstractyes

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