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Record W3087676245 · doi:10.1188/20.cjon.538-546

Characterizing Pain Experiences: African American Patients With Multiple Myeloma Taking Around-the-Clock Opioids

2020· article· en· W3087676245 on OpenAlexfundno aff
Sarah M. Belcher, Deborah Watkins Bruner, Craig C. Hofmeister, Jaime Kweon, Salimah H. Meghani, Katherine A. Yeager

Bibliographic record

VenueClinical journal of oncology nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
FundersOncolytics BiotechNational Institute of Nursing ResearchSanofi
KeywordsMedicineMultiple myelomaHematologic malignancyMalignancyAfrican americanEpidemiologyInternal medicineCancerOncologyFamily medicineEthnology

Abstract

fetched live from OpenAlex

BACKGROUND: Despite known disparities by race, studies to date have not focused on pain characterization among African American patients with multiple myeloma. OBJECTIVES: This study aimed to characterize the pain experience, beliefs about pain and pain control, and additional symptoms among African American patients with multiple myeloma taking around-the-clock opioids. METHODS: This study employed secondary analysis of baseline data from a completed longitudinal study of opioid adherence. Descriptive statistics were used to characterize the sample, pain experience, beliefs regarding pain and pain control, and related symptoms. FINDINGS: Participants (N = 34) experienced everyday pain and additional symptoms, and half experienced depression. Pain management barriers included dislike of pills, fear of addiction, and bothersome side effects from pain and medication. Additional larger studies can incorporate multilevel factors contributing to high symptom burden.

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)0.005
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 score0.442
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.078
GPT teacher head0.402
Teacher spread0.324 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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