Prescription Opioid Medication Survey: A Tool to Collect Deep Phenotypic Data on the Multifactorial Pathways to Opioid Use Disorder in Clinical and Population-Based Cohorts
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.
Bibliographic record
Abstract
Introduction: We are in the midst of an opioid epidemic. In the USA, more than a third of the country knows someone who has died from an opioid overdose. Prescription opioids (e.g., oxycodone, hydrocodone, and fentanyl) are commonly used and misused, and it has been estimated that approximately 8-12% of individuals who misuse opioids will subsequently develop an opioid use disorder (OUD). While emphasis has been placed on understanding OUD and the associated adverse effects, there remains a critical gap in systematically characterizing the multifactorial pathways (e.g., behavioral, clinical, genetic, and socio-demographic characteristics) that contribute to the transition from initial use to misuse to OUD. Methods: To address this gap, we introduce the Prescription Opioid Medication Survey (POMS), an online 120-item assessment that compiles multiple validated and standardized instruments. POMS is intended for individuals with any lifetime prescription opioid use. POMS captures various aspects of prescription opioid use including data on opioid use patterns, subjective effects (e.g., euphoria, nausea), problematic use, withdrawal, OUD, overdose, treatment history, and remission. It also addresses comorbid risk factors such as surgical history, chronic pain, other substance use disorders (SUD; e.g., nicotine, alcohol, cannabis, stimulants), other addictive behaviors (i.e., gambling, sexual behaviors, and gaming), and family history of SUD and other addictive behaviors. Mental health assessments, including screening for depression and anxiety, self-reports of eight psychiatric disorders (anxiety, depression, bipolar, schizophrenia, attention-deficit/hyperactivity disorder, post-traumatic stress disorder, obsessive-compulsive disorder, eating disorders), and related mental health conditions (e.g., loneliness, suicide, trauma) are included, along with data on personality traits (e.g., risk-taking, delay discounting, wisdom) and socio-demographic factors. POMS is intended to be administered in clinical settings and large population-based cohorts, facilitating data collection that can enable discoveries to inform better prevention and intervention strategies for OUD. Conclusion: POMS offers a comprehensive tool for systematically capturing the multifactorial risk factors associated with opioid misuse and OUD, providing insights that can inform prevention and intervention strategies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it