Pain and Addiction: Managing Risk Through Comprehensive Care
Bibliographic record
Abstract
The use of controlled substances, including opioids, in people who may suffer from concurrent substance use disorders presents challenges to the healthcare professional. Pain and addiction can coexist either as a continuum or separate comorbid conditions. Success in the treatment of either condition requires an approach that encompasses the biopsychosocial needs of the patient. In pain management, controlled substances can be either the problem or the solution, depending on the healthcare professional's training and perspective. Not all patients on opioid pharmacotherapy do well. Some, with inadequate treatment responses, may actually improve on discontinuation of their opioids. Therefore, in any trial of pharmacotherapy, there must be a clear exit strategy as part of the treatment plan. The goal of this article is to explore the importance of making reasoned clinical decisions when faced with aberrant behavior, which is when the patient steps outside the boundaries of the agreed on treatment plan and is established as early as possible in the doctor-patient relationship. In this case, it is essential to separate the "motive" from the "problematic behavior" when trying to interpret the implications of aberrant behavior rather than simply applying a diagnostic label of addiction, which may or may not be correct.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".