Review of the lumbar infusion test use in pediatric populations: state-of-the-art and future perspectives
Bibliographic record
Abstract
BACKGROUND: The lumbar infusion test (LIT) is a routine part of the diagnostic process of various CSF dynamics disorders in adults. However, it is rarely used in the paediatric population due to a lack of evidence substantiating its efficacy and overall indications. METHODS: Articles utilizing the LIT in a paediatric cohort (≤ 18 years) were included according to the PRISMA guidelines with the Newcastle-Ottawa Scale to assess the risk of bias. This review was registered at PROSPERO database under number: CRD42024625857. RESULTS: A total of 15 studies, yielding 441 patients, were included in the review. The most common indications for LIT were to predict shunt responsiveness in hydrocephalus and idiopathic intracranial hypertension (IIH). In IIH, the interaction between cerebrospinal fluid pressure (CSFp) and sagittal sinus pressure (SSp) may offer valuable diagnostic insights and present a novel assessment approach. The LIT is a validated tool, especially effective for predicting shunt responsiveness and detecting malfunctions in both IIH and hydrocephalus. CONCLUSIONS: Data surrounding LIT usage in children is lacking and most studies are outdated. Caution is needed when interpreting resistance to outflow (Rout) due to potential overestimation, with more attention directed to CSFp and the pressure within the venous system coupling in IIH. Future studies should focus on standardizing LIT protocols across age groups with focusing more on signal characteristics rather than individual parameters and fostering interdisciplinary collaboration to optimize diagnostic accuracy.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".