Oblique Intrathecal Injection in Lumbar Spine Surgery: A Technical Note
Bibliographic record
Abstract
OBJECTIVES: Intrathecal morphine (ITM) is an efficacious method of providing postoperative analgesia and reducing pain associated complications. Despite adoption in many surgical fields, ITM has yet to become a standard of care in lumbar spine surgery. Spine surgeons' reticence to make use of the technique may in part be attributed to concerns of precipitating a cerebrospinal fluid (CSF) leak. METHODS: Herein we describe a method for oblique intrathecal injection during lumbar spine surgery to minimize risk of CSF leak. The dural sac is penetrated obliquely at a 30° angle to offset dural and arachnoid puncture sites. Oblique injection in instances of limited dural exposure is made possible by introducing a 60° bend to a standard 30-gauge needle. RESULTS: The technique was applied for injection of ITM or placebo in 104 cases of lumbar surgery in the setting of a randomized controlled trial. Injection was not performed in two cases (2/104, 1.9%) following preinjection dural tear. In the remaining 102 cases no instances of postoperative CSF leakage attributable to oblique intrathecal injection occurred. Three cases (3/102, 2.9%) of transient CSF leakage were observed immediately following intrathecal injection with no associated sequelae or requirement for postsurgical intervention. In two cases, the observed leak was repaired by sealing with fibrin glue, whereas in a single case the leak was self-limited requiring no intervention. CONCLUSIONS: Oblique dural puncture was not associated with increased incidence of postoperative CSF leakage. This safe and reliable method of delivery of ITM should therefore be routinely considered in lumbar spine surgery.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".