Traumatic Neonatal Lumbar Punctures: Experience at a Large Pediatric Tertiary Care Center in Canada
Bibliographic record
Abstract
Objective Contamination of cerebrospinal fluid (CSF) by blood during neonatal lumbar puncture (LP) is common and poses diagnostic difficulties. Our objectives were to determine the number of traumatic LPs performed at the BC Children's Hospital over 9 years and whether there was an association between traumatic LPs and demographic variables, hospital location, or time of the procedure. Study Design This study was a retrospective review of neonatal CSF samples from May 2006 to March 2015. The data were analyzed to establish the rate of traumatic samples and whether there was an association between traumatic LPs and demographic variables (age, gender), location of procedure, positive CSF culture, and/or timing of the procedure. Results A total of 1,263 LPs were reviewed, 47.7% (n = 602) were contaminated with >400 red blood cells/high-power field. The median age of neonates whose samples were uncontaminated was 10.580 days compared with 6.535 days in the group with contaminated samples (z = − 2.884, p = 0.004). None of the other factors studied was associated with traumatic taps. Detected organisms included Escherichia coli (n = 12), coagulase-negative Staphylococcus (n = 7), Enterococcus faecalis (n = 3), and group B Streptococcus (n = 2). Conclusion Nearly half of all CSF samples in the study period were contaminated. Traumatic samples were more common in younger neonates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".