Analgesic medication considerations for chronic pain management post-bariatric surgery
Bibliographic record
Abstract
INTRODUCTION: Bariatric surgery, an option for obesity management, can significantly alter gastrointestinal structure and processes. These changes can impact the pharmacokinetics (PK) of medications, which can translate to clinical differences in efficacy and safety. Chronic pain is prevalent in obesity and often persists post-bariatric surgery. AREAS COVERED: This narrative review examines the PubMed literature from 1990 to January 2024 for the impact of bariatric surgery on the management of chronic pain medications including non-opioid (acetaminophen, non-steroidal anti-inflammatory drugs, antidepressants, and cannabinoids) and opioid medications. EXPERT OPINION: An individualized medication management approach is ideal for post-bariatric surgery patients, as PK parameters, type of surgery, time since surgery, and patient-specific factors make it difficult to support blanket recommendations. Close monitoring of efficacy and safety outcomes is essential in chronic pain management. While the PK of acetaminophen and opioids are impacted, the value of these medications in the setting of chronic pain is dwindling as more efficacy and safety data emerges. A life-long ban of NSAIDs due to marginal ulcer risk is not endorsed; rather, we advocate for shifting the focus to marginal ulcer prevention strategies, individualized benefit-risk analysis, and safety monitoring using surrogate markers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".