Intrathecal morphine in lumbar spine surgery: a novel injection technique
Bibliographic record
Abstract
Background: Intrathecal morphine (ITM) is an efficacious method of providing post-operative analgesia. Despite adoption in many surgical fields, ITM has yet to become a standard of care in lumbar spine surgery. This may in part be attributed to concerns over precipitating a cerebrospinal fluid (CSF) leak following dural puncture. Methods: The dural sac is penetrated obliquely at a 30° angle to prevent overlap of 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. Participating spinal surgeons were provided with brief instructions outlining the injection technique. Adherence and complications were collected prospectively. Results: The technique was applied to 98 cases of elective lumbar fusion at our institution. Two cases (2.0%) of non-adherence followed pre-injection dural tear. 96 cases of oblique ITM injection resulted in no attributable instances of post-operative CSF leakage. Two cases (2.1%) of transient, self-limited CSF leakage immediately following ITM injection were observed without associated sequelae or requirement for further intervention. Conclusions: Oblique dural puncture is not associated with increased incidence of post-operative CSF leakage. This safe and reliable method of delivery of ITM should 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".