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Record W4396829126 · doi:10.1186/s12887-024-04798-9

Antibiotic impregnated catheters and intrathecal antibiotics for CSF shunt infection prevention in children undergoing low-risk CSF shunt surgery

2024· article· en· W4396829126 on OpenAlexaff
Stacey Podkovik, Chuan Zhou, Susan Coffin, Matt Hall, Jason S. Hauptman, Matthew P. Kronman, Francesco T. Mangano, Ian F. Pollack, Sabrina Sedano, Joaquin Vega, Joshua K. Schaffzin, Emily Thorell, Benjamin C. Warf, Kathryn B. Whitlock, Tamara D. Simon

Bibliographic record

VenueBMC Pediatrics · 2024
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsUniversity of Ottawa
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeUniversity of WashingtonChildren's Hospital of PittsburghNational Institutes of HealthCincinnati Children's Hospital Medical CenterSeattle Children's Research InstituteChildren's Hospital of PhiladelphiaUniversity of Southern California
KeywordsMedicineHydrocephalusAntibioticsShunt (medical)Propensity score matchingExternal ventricular drainRetrospective cohort studySurgeryOdds ratioPopulationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cerebrospinal fluid (CSF) shunts allow children with hydrocephalus to survive and avoid brain injury (J Neurosurg 107:345-57, 2007; Childs Nerv Syst 12:192-9, 1996). The Hydrocephalus Clinical Research Network implemented non-randomized quality improvement protocols that were shown to decrease infection rates compared to pre-operative prophylactic intravenous antibiotics alone (standard care): initially with intrathecal (IT) antibiotics between 2007-2009 (J Neurosurg Pediatr 8:22-9, 2011), followed by antibiotic impregnated catheters (AIC) in 2012-2013 (J Neurosurg Pediatr 17:391-6, 2016). No large scale studies have compared infection prevention between the techniques in children. Our objectives were to compare the risk of infection following the use of IT antibiotics, AIC, and standard care during low-risk CSF shunt surgery (i.e., initial CSF shunt placement and revisions) in children. METHODS: A retrospective observational cohort study at 6 tertiary care children's hospitals was conducted using Pediatric Health Information System + (PHIS +) data augmented with manual chart review. The study population included children ≤ 18 years who underwent initial shunt placement between 01/2007 and 12/2012. Infection and subsequent CSF shunt surgery data were collected through 12/2015. Propensity score adjustment for regression analysis was developed based on site, procedure type, and year; surgeon was treated as a random effect. RESULTS: A total of 1723 children underwent initial shunt placement between 2007-2012, with 1371 subsequent shunt revisions and 138 shunt infections. Propensity adjusted regression demonstrated no statistically significant difference in odds of shunt infection between IT antibiotics (OR 1.22, 95% CI 0.82-1.81, p = 0.3) and AICs (OR 0.91, 95% CI 0.56-1.49, p = 0.7) compared to standard care. CONCLUSION: In a large, observational multicenter cohort, IT antibiotics and AICs do not confer a statistically significant risk reduction compared to standard care for pediatric patients undergoing low-risk (i.e., initial or revision) shunt surgeries.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.265
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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