A safety review of approved intrathecal analgesics for chronic pain management
Bibliographic record
Abstract
Introduction: Intrathecal (IT) drug therapy is an effective treatment option for patients with chronic pain of malignant or nonmalignant origin, with an established safety profile and fewer adverse effects compared to oral or parenteral pain medications. Morphine (a μ-opioid receptor agonist) and ziconotide (a non-opioid calcium channel antagonist) are the only IT agents approved by the U.S. Food and Drug Administration for the treatment of chronic pain. Although both are considered first-line IT therapies, each drug has unique properties and considerations.Areas Covered: This review will evaluate the pivotal trials that established the use of morphine and ziconotide as first-line IT therapy for patients with chronic pain, as well as safety and efficacy data generated from various retrospective and prospective studies.Expert Opinion: Morphine and ziconotide are effective IT therapies for patients with chronic malignant or nonmalignant pain that is refractory to other interventions. IT ziconotide is recommended as a first-line therapy due to its efficacy and avoidance of many adverse effects commonly associated with opioids. The use of IT morphine is also considered first-line; however, the risks of respiratory depression, withdrawal with drug discontinuation or pump malfunction, and the development of tolerance require careful patient selection and management.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.008 | 0.002 |
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".