Predictive value of repeated cerebrospinal fluid parameters in the outcomes of bacterial meningitis in infants <90 days of age
Bibliographic record
Abstract
BACKGROUND: There are variations in recommendations from different guidelines regarding the indications for repeat lumbar puncture (LP) in young infants with the diagnosis of bacterial meningitis. OBJECTIVE: To evaluate the frequency of repeat LPs and the characteristics of cerebrospinal fluid (CSF) parameters in repeated sampling and their predictive values for adverse outcomes in a national cohort. METHODS: This cohort study included infants born January 1, 2013 through December 31, 2014, who had proven or suspected bacterial meningitis within the first 90 days of life at seven paediatric tertiary care hospitals across Canada, and who underwent a repeat LP at the discretion of the treating physicians. RESULTS: Forty-nine of 111 infants (44%) underwent repeat LP at a median of 5 (IQR: 3, 13) days after the LP that led to the diagnosis of bacterial meningitis. Those who had meningitis caused by gram negative bacilli were more likely to have repeat LP than those with gram positive bacteria (77% versus 57%; p = 0.012). White blood cell (WBC) count on the second spinal tap yielded an area under the curve of 0.88 for predicting sequelae of meningitis at discharge from the hospital, with a cut-off value of 366 × 106/L, providing a sensitivity of 91% and specificity of 88%. CONCLUSION: In this multi-centre retrospective cohort study, infants with gram negative meningitis were more likely to have repeated LP. A high WBC on the second CSF sample was predictive of adverse outcome at the time of discharge from the hospital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".