Ultrasound imaging versus palpation method for diagnostic lumbar puncture in neonates and infants: a systematic review and meta-analysis
Bibliographic record
Abstract
IMPORTANCE: Lumbar puncture (LP) failure rates vary and can be as high as 65%. Ultrasound guidance could increase the success of performing LP. OBJECTIVE: To summarise the evidence on the use of ultrasound guidance versus palpation method for LP. DATA SOURCES: We searched computerised databases and published indexes, registries and references identified from bibliographies of pertinent articles without any language restrictions to find studies that compared ultrasound guidance to palpation method for performing an LP. STUDY SELECTION: Studies were included if they were randomised or quasirandomised trials in neonates and infants that compared ultrasound guidance with palpation method for performing an LP. DATA EXTRACTION AND SYNTHESIS: Standardised data collection tool was used for data extraction, and two reviewers independently assessed the quality of the studies. MAIN OUTCOMES AND MEASURES: The primary outcome was the risk of LP failure, while the risk of traumatic tap, needle redirections/reinsertions and procedure durations were secondary outcomes. RESULTS: Data from four studies and 308 participants is included in the analysis. Ultrasound imaging reduced the risk of LP failure, risk ratio of 0.58 (95% CI 0.15 to 2.28), but it was not statistically significant (p=0.44). Ultrasound imaging significantly reduced the risk of a traumatic tap risk ratio of 0.33 (95% CI 0.13 to 0.82) and p=0.02. The included studies had low to moderate quality; the studies differed based on mean age and with variability on outcome definition. CONCLUSIONS AND RELEVANCE: This meta-analysis suggests that ultrasound imaging has no effect in increasing lumbar success but is beneficial in reducing the risk of traumatic taps in neonates and infants. TRIAL REGISTRATION NUMBER: CRD42017055800.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".