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Record W4401165482 · doi:10.2196/57199

Impact of Patient Personality on Adherence to Oral Anticancer Medications: An Opportunity?

2024· article· en· W4401165482 on OpenAlexvenueno aff
Mahtab Jafari, Alex Shahverdian, Gelareh Sadigh, Richard A. Van Etten

Bibliographic record

VenueJMIR Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsPersonalityMedicineClinical psychologyMedication adherencePsychologyPsychiatryInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Adherence to prescribed oral anticancer therapy is an important determinant of patient outcomes, including progression-free and overall survival. While many factors (eg, medication side effects and out-of-pocket costs, problems with insurance authorization, and timely medication refills) can affect adherence, one that is relatively unexplored is the impact of a patient's attitude and personality. Patient personality influences medication adherence and persistence in nonmalignant chronic conditions such as cardiovascular disease and diabetes. In breast cancer and chronic myeloid leukemia, studies suggest that personality also affects adherence to oral chemotherapy which can be targeted to improve adherence. In this viewpoint, we highlight the opportunity of incorporating patient personality as interventions to oral cancer therapy adherence and discuss current barriers to implementation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.985

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)0.0160.000

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 head0.123
GPT teacher head0.472
Teacher spread0.349 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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