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Record W3118701973 · doi:10.1001/jamaoncol.2020.6789

Frequency of and Factors Associated With Nonmedical Opioid Use Behavior Among Patients With Cancer Receiving Opioids for Cancer Pain

2021· article· en· W3118701973 on OpenAlexaboutno aff
Sriram Yennurajalingam, Joseph Arthur, Suresh Reddy, Tonya Edwards, Zhanni Lu, Aline Rozman de Moraes, Susamma M. Wilson, Elif Erdoğan, Manju P Joy, Shirley Darlene Ethridge, Leela Kuriakose, Jimi S. Malik, John M Najera, Saima Rashid, Qian Yu, Michal Kubiak, Kristy Nguyen, Jimin Wu, David Hui, Éduardo Bruera

Bibliographic record

VenueJAMA Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineInterquartile rangeCancerOpioidInternal medicineCancer painInclusion and exclusion criteriaPhysical therapyAlternative medicine

Abstract

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IMPORTANCE: One of the main aims of research on nonmedical opioid use (NMOU) is to reduce the frequency of NMOU behaviors through interventions such as universal screening, reduced opioid exposure, and more intense follow-up of patients with elevated risk. The absence of data on the frequency of NMOU behavior is the major barrier to conducting research on NMOU. OBJECTIVE: To determine the overall frequency of and the independent predictors for NMOU behavior. DESIGN, SETTING, AND PARTICIPANTS: In this prognostic study, 3615 patients with cancer were referred to the supportive care center at MD Anderson Cancer Center from March 18, 2016, to June 6, 2018. Patients were eligible for inclusion if they had cancer and were taking opioids for cancer pain for at least 1 week. Patients were excluded if they had no follow-up within 3 months of initial consultation, did not complete the appropriate questionnaire, or did not have scheduled opioid treatments. After exclusion, a total of 1554 consecutive patients were assessed for NMOU behavior using established diagnostic criteria. All patients were assessed using the Edmonton Symptom Assessment Scale, the Screener and Opioid Assessment for Patients with Pain (SOAPP), and the Cut Down, Annoyed, Guilty, Eye Opener-Adapted to Include Drugs (CAGE-AID) survey. Data were analyzed from January 6 to September 25, 2020. RESULTS: A total of 1554 patients (median [interquartile range (IQR)] age, 61 [IQR, 52-69] years; 816 women [52.5%]; 1124 White patients [72.3%]) were evaluable for the study, and 299 patients (19.2%) had 1 or more NMOU behaviors. The median (IQR) number of NMOU behaviors per patient was 1 (IQR, 1-3). A total of 576 of 745 NMOU behaviors (77%) occurred by the first 2 follow-up visits. The most frequent NMOU behavior was unscheduled clinic visits for inappropriate refills (218 of 745 [29%]). Eighty-eight of 299 patients (29.4%) scored 7 or higher on SOAPP, and 48 (16.6%) scored at least 2 out of 4 points on the CAGE-AID survey. Results from the multivariate model suggest that marital status (single, hazard ratio [HR], 1.58; 95% CI, 1.15-2.18; P = .005; divorced, HR, 1.43; 95% CI, 1.01-2.03; P = .04), SOAPP score (positive vs negative, HR, 1.35; 95% CI, 1.04-1.74; P = .02), morphine equivalent daily dose (MEDD) (HR, 1.003; 95% CI, 1.002-1.004; P < .001), and Edmonton Symptom Assessment Scale pain level (HR, 1.11; 95% CI, 1.06-1.16; P < .001) were independently associated with the presence of NMOU behavior. In recursive partition analysis, single marital status, MEDD greater than 50 mg, and SOAPP scores greater than 7 were associated with a higher risk (56%) for the presence of NMOU behavior. CONCLUSIONS AND RELEVANCE: This prognostic study of patients with cancer taking opioids for cancer pain found that 19% of patients developed NMOU behavior within a median duration of 8 weeks after initial supportive care clinic consultation. Marital status (single or divorced), SOAPP score greater than 7, higher levels of pain severity, and MEDD level were independently associated with NMOU behavior. This information will assist clinicians and investigators designing clinical and research programs in this important field.

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)0.001
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 score0.016
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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

Citations66
Published2021
Admission routes1
Has abstractyes

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