Ultrasound imaging for lumbar punctures and epidural catheterisations: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To determine whether ultrasound imaging can reduce the risk of failed lumbar punctures or epidural catheterisations, when compared with standard palpation methods, and whether ultrasound imaging can reduce traumatic procedures, insertion attempts, and needle redirections. DESIGN: Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES: Ovid Medline, Embase, and Cochrane Central Register of Controlled Trials up to May 2012, without restriction by language or publication status. REVIEW METHODS: Randomised trials that compared ultrasound imaging with standard methods (no imaging) in the performance of a lumbar puncture or epidural catheterisation were identified. RESULTS: 14 studies with a total of 1334 patients were included (674 patients assigned to the ultrasound group, 660 to the control group). Five studies evaluated lumbar punctures and nine evaluated epidural catheterisations. Six of 624 procedures conducted in the ultrasound group failed; 44 of 610 procedures in the control group failed. Ultrasound imaging reduced the risk of failed procedures (risk ratio 0.21 (95% confidence interval 0.10 to 0.43), P<0.001). Risk reduction was similar when subgroup analysis was performed for lumbar punctures (risk ratio 0.19 (0.07 to 0.56), P=0.002) or epidural catheterisations (0.23 (0.09 to 0.60), P=0.003). Ultrasound imaging also significantly reduced the risk of traumatic procedures (risk ratio 0.27 (0.11 to 0.67), P=0.005), the number of insertion attempts (mean difference -0.44 (-0.64 to -0.24), P<0.001), and the number of needle redirections (mean difference -1.00 (-1.24 to -0.75), P<0.001). CONCLUSIONS: Ultrasound imaging can reduce the risk of failed or traumatic lumbar punctures and epidural catheterisations, as well as the number of needle insertions and redirections. Ultrasound may be a useful adjunct for these procedures.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".