Postoperative cerebrospinal fluid wound leakage as a predictor of shunt infection: a prospective analysis of 205 cases
Bibliographic record
Abstract
OBJECT: The purpose of this study was to audit some of the risk factors for CSF shunt infections within the authors' practice and analyze the statistical significance of these factors. METHODS: The authors used their own contemporaneously collected shunt database in this study. All shunt procedures performed over a 2-year period between March 2000 and February 2002 at Great Ormond Street Hospital, London, were analyzed. For the purposes of this study, positive CSF cultures were a prerequisite for a data set to qualify as a shunt infection. The authors studied the effects of patient age, the etiology of hydrocephalus, whether the surgery was primary shunt placement versus a revision, the surgeon's level of experience, whether the surgery was performed on an elective or emergency basis, and the presence or absence of a perioperative CSF leak. Statistical analyses were performed. RESULTS: Two hundred and five patients with a mean (+/- SD) age at surgery of 27.9 +/- 43.0 months were included in this study. Shunt infections developed in 17 patients (8.3%) at a median of 42 days postoperatively (range 14-224 days). The presence of a perioperative CSF leak was the only variable that showed a statistically significant association with the occurrence of a shunt infection, with an infection rate of 57.1% compared to 4.7% in cases with no leak (OR 27.0 [95% CI 7.7-94.3]). The cause of hydrocephalus, elective versus emergency surgery, level of surgeon experience, a primary versus a revision procedure, and patient age did not have a bearing on the infection risk. CONCLUSIONS: The presence of a perioperative CSF leak puts pediatric patients at a very high risk of shunt infection. Aside from prevention, the optimal management of such CSF leaks require further investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".