Bibliographic record
Abstract
Implanted drug delivery system {IDDS} have emerged as a promising strategy for managing chronic pain, offering precise and sustained drug administration to achieve optimal pain relief while minimizing adverse effects. This abstract reviews the key aspects of IDDS in the context of chronic pain control. Chronic pain, characterized by its persistence over extended periods, presents a significant challenge in medical practice due to its complex and multifaceted nature. Traditional oral medication often fall short of providing consistent pain relief while avoiding systemic side effects. IDDS addresses these limitations by delivering drugs directly to the target site, bypassing first-pass metabolism, and maintaining steady therapeutic concentrations. IDDS consists of implantable device that houses a reservoir of the chosen medication, connected to a catheter for drug release. The release rate can be programmed and adjusted according to the patient's needs. Commonly used drugs include opioids, local anesthetics, and anti-inflammatory agents. The implantation procedure requires surgical expertise but offers the advantage of long-term pain management, reducing the need for frequent dosing. The efficacy of IDDS in chronic pain control has been demonstrated in various conditions such as cancer pain, neuropathic pain, and failed back surgery syndrome. By providing sustained drug delivery, IDDS ensures consistent pain relief, potentially improving patients' quality of life and reducing the development of tolerance and dependence. However, challenges include the risk of infection, device malfunction, and the invasiveness of the implantation procedure.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".