Cerebrospinal fluid shunt infection: a prospective study of risk factors
Bibliographic record
Abstract
OBJECT: Hydrocephalus is a common condition of childhood that usually requires insertion of a cerebrospinal fluid (CSF) shunt. Infection is one of the most devastating complications that may arise from the presence of CSF shunts. In this study, the authors prospectively analyzed perioperative risk factors for CSF shunt infection in a cohort of children. METHODS: Between 1996 and 1999, 299 eligible patients underwent CSF shunt operations (insertions and revisions) that were observed by a research nurse at a tertiary care pediatric hospital. Several perioperative variables were recorded. All cases were followed postoperatively for 6 months to note any development of CSF shunt infection. A Cox proportional hazards model was used to analyze the relationship between the variables and the development of shunt infection. Thirty-one patients (10.4%) experienced shunt infection. Three perioperative variables were significantly associated with an increased risk of shunt infection: 1) the presence of a postoperative CSF leak (hazard ratio [HR] 19.16, 95% confidence interval [CI] 6.96-52.91); 2) patient prematurity (< 40 weeks' gestation at the time of shunt surgery: HR 4.72, 95% CI 1.71-13.06), and 3) the number of times the shunt system was inadvertently exposed to breached surgical gloves (HR 1.07, 95% CI 1.02-1.12). CONCLUSIONS: Three variables associated with an increased incidence of shunt infection have been identified. Changes in clinical practice should address these variables, as follows. 1) Great care should be taken intraoperatively to avoid a postoperative CSF leak. 2) Alternatives to placement of a CSF shunt in premature infants should be studied. 3) Surgeons should minimize manual contact with the shunt system and consider the use of double gloves.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".