Bibliographic record
Abstract
PURPOSE OF REVIEW: The present study discusses the utilization of neuraxial drug delivery (NDD) for the management of cancer pain, based on recent trials, reviews, and guidelines with a focus on cost analysis. RECENT FINDINGS: Almost all recent publications suggest that more stringent research is needed to improve evidence on NDD, particularly as conflicting reports exist regarding cost effectiveness of drug delivery systems. The combination of local anesthetics and opioids, with or without clonidine, continues to be reported as beneficial with the utilization of patient controlled systems providing an advantage over continuous ones. Interestingly, the use of opioids as an adjunct to local anesthetics may not enhance analgesia but the addition of dexamethasone is useful for incident cancer-related bone pain. Ziconitide remains supported as first-line therapy in districts where it is available - United States and Europe. Although new targeted drugs are being designed for cancer pain management, none have seen human clinical trials in the last year. SUMMARY: The ability to demonstrate cost effectiveness of NDD is variable from region to region. Less expensive externalized systems may pose a viable alternative. With the exception of dexamethasone, no new drugs have been shown to provide any benefit to conventional medications.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".