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Record W2911933862 · doi:10.2147/jpr.s171742

<p>Understanding factors that contribute to the disposal of unused opioid medication</p>

2019· article· en· W2911933862 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Pain Research · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsPurdue Pharma (Canada)
FundersPurdue PharmaPurdue University
KeywordsMedicineOpioidDiscontinuationDrugControlled substanceEmergency medicineMedical emergencyIntensive care medicinePharmacologyPsychiatryMedical prescriptionInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Drivers of excess controlled substance disposal behaviors are not well understood. A survey of patients who had received opioid-based medications was conducted to inform the design of future innovative drug take-back programs. METHODS: This was a cross-sectional survey study conducted in 152 participants who received treatment with an opioid within the previous 2 years and had possession of unused medication following either switching to a different opioid or discontinuation of pain. RESULTS: Approximately one-third of patients had disposed of their unused opioid medication. Education about the importance of and appropriate methods for drug disposal was associated with a significantly increased likelihood of patients disposing of unused medication, and it was observed that patients prescribed an immediate-release/short-acting opioid were twice as likely to keep their medication compared to those prescribed an extended-release/long-acting opioid. The most commonly reported methods for disposal were via drug return kiosks and flushing the medication down the toilet. Some of the most impactful drivers of unused opioid disposal were routine practice of disposing of all unused drugs and instruction from a health care provider, and the most common driver of keeping unused medication was the desire to have it on-hand should there be a need to treat pain in the future. Over 80 % of patients indicated that they would be more likely to use a drug take-back service if they were offered compensation or if the kiosk was in a location that they visited frequently, and approximately half of the patients indicated that they would be willing to request an initial partial fill of an opioid prescription to reduce the volume of unused medication. CONCLUSION: There is a clear need to increase patient awareness about the importance and methods of proper medication disposal, and a great opportunity for health care providers to increase patient education efforts. These study findings also highlight key areas for improvement in drug take-back programs that may promote and incentivize more patients to utilize the services.

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.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.052
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 integrity0.0000.001
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 head0.086
GPT teacher head0.373
Teacher spread0.287 · 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