Anesthesiologist preference for postoperative analgesia in major surgery patients with obstructive sleep apnea
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is prevalent and presents perioperative challenges. There are guidelines regarding perioperative care of OSA, but analgesia management of OSA patients is inconsistent or inadequate. This is a study of the United Kingdom anesthesiologists' postoperative analgesia preferences for OSA patients. Overall, the 1st choice of main analgesia was continuous epidural local anesthetic (LA) without opioid, at 30% rate; P = 0.001. The 2nd choice was continuous epidural LA plus fentanyl, at 21% rate; P = 0.001. The 3rd choice was intrathecal diamorphine, at 19% rate; P = 0.001. The 4th choice was nerve block catheter LA infusion, at 13% rate; P = 0.001. The 5th choice was wound infiltration with LA ± epinephrine, at 8% rate; P = 0.001. The 6th choice was systemic opioid, at 7% rate; P = 0.007. The 7th choice was systemic nonsteroidal anti-inflammatory drugs, at 2% rate; P = 0.001. The hospital setting or anesthesiologists' experience did not significantly impact analgesia choice: P =0.411. This study shows that current practice by anesthesiologists has a preference for regional or opioid-sparing analgesia for OSA patients. This safe approach conforms to guidelines and should be encouraged.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".