Bibliographic record
Abstract
Opioids remain the main pharmacological tools for pain control in the postoperative patient. Recent concerns about chronic use of non-steroidal anti-inflammatory drugs have put extra pressure on health care workers to device and develop new medications and delivery methods to provide patients with appropriate pain relief after surgery. New technologies and better understanding of the pharmacology of the opioids administered by non-traditional ways will be reviewed in this manuscript. Understanding the anatomy of the nose cavity is important to use the inhaled way to deliver fentanyl, sufentanyl or butorphanol in surgical patients. High concentration and small volume are keys to good absorption and effect by nasal administration. Transdermal delivery of fentanyl has been used in chronic pain for some years. A new fentanyl self-administration method is in advanced trials for clinical use. It has a huge potential in giving good pain relief with lower side effects and more patient independence because of its reduced size and lack of tubing attached to it. Day surgery patients could be great candidates for this therapeutic alternative. Finally, oral transmucosal fentanyl is also in the market. Better suited to be used in controlling breakthrough pain in chronic settings, it has been so far marketed in the perioperative period. Side effects are a concern in its use for preoperative sedation in children. Large studies are now required for drugs approval and for new indications at regulatory agencies level. Good clinical judgment is more relevant now than ever before to avoid complications and new withdraw of old good medications inappropriately prescribed from the market place.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.041 |
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".