Adjunct continuous intravenous ketamine infusion for postoperative pain relief following posterior spinal instrumentation for correction of scoliosis: a case report
Bibliographic record
Abstract
Providing effective analgesia is challenging for correction of idiopathic scoliosis, as nonsteroidal anti-inflammatory drugs and epidural anesthesia are controversial and large-dose opioids can cause significant side effects. Perioperative adjuvant low-dose ketamine has been shown to provide good supplementary analgesia as well as to potentially spare opioid consumption. Ketamine may also improve early ease of mobility without addition of any noticeable adverse effects. This case describes the combined use of a continuous low-dose ketamine infusion and patient-controlled analgesia (PCA) morphine for postoperative analgesia in an adolescent girl undergoing posterior spinal instrumentation and correction of scoliosis. The patient had excellent postoperative analgesia and was able to participate in early rehabilitation. The opioid-sparing effect of ketamine was not demonstrated in this case. Further study of continuous low-dose ketamine infusions in this patient population would be beneficial to provide more evaluation of the efficacy and tolerability of ketamine and of its opioid-sparing potential.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".