Infection of Cerebrospinal Fluid Drainage Devices
Bibliographic record
Abstract
BACKGROUND: Ventricular reservoir infections and cerebrospinal fluid (CSF) shunt infections are diagnosed when bacteria are recovered from microbiological cultures of CSF samples from these devices. We applied high throughput sequencing (HTS) to understand the course of changes in ventricular reservoir and shunt infection microbiota. OBJECTIVES: Evaluate the utility of monitoring microbiota in CSF (1) from ventricular reservoirs to detect development of an infection and (2) during treatment of CSF shunt infections to assess treatment response. METHODS: Study populations included (1) neonates with temporizing ventricular reservoirs who developed reservoir infection and (2) children undergoing treatment for conventional culture-confirmed CSF shunt infection. The V4 region of the 16S ribosomal RNA gene was amplified and sequenced. Comparison of taxonomic results of HTS with standard microbiological culture results (when available) was described for each CSF sample. A robust HTS signal was defined by a microbial load of ≥1e5 microbial genome equivalents/mL. RESULTS: In none of the five ventricular reservoir infection cases was there a robust HTS signal for the responsible bacteria immediately prior to infection. In six of the seven CSF shunt infection cases, there was a robust HTS signal for the genus of the responsible bacteria in the sample at the time of positive CSF culture. The proportion of sequences from the genus associated with the responsible bacteria decreased during infection treatment. CONCLUSIONS: These pilot data suggest limited utility in using HTS for surveillance for ventricular reservoir infections, as they emerge abruptly. In CSF shunt infection, HTS demonstrates a return to heterogeneous microbiota when bacterial cultures become negative.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".