An outbreak after all: <i>Cutibacterium acnes</i> among pediatric patients with cerebrospinal fluid diversion device infections highlights gaps in guidelines
Bibliographic record
Abstract
Abstract Objective: Cutibacterium acnes is normal skin flora but can cause sterile implant infections. We investigated a cluster of seven patients with C. acnes in anaerobic cerebrospinal fluid (CSF) cultures in November 2020. Further analysis identified a missed outbreak, highlighting ambiguity in diagnosis of indolent organisms in the 2017 IDSA meningitis guidelines. Design: Outbreak investigation. Setting: Quaternary pediatric facility. Patients: A case was defined as a hospitalized patient with C. acnes isolated from CSF culture from January 1, 2016 to December 31, 2022. Methods: We defined comparison periods based on timing of C. acnes culture positivity as 1) pre-outbreak (2016–2020), 2) outbreak (2020–2021), and 3) post-outbreak (2022). Rates of C. acnes positive cultures per 1000 CSF cultures and rate ratios were calculated by comparison periods. Results: We identified 9 positive C. acnes CSF cultures among 7 cases November 10–27, 2020, all with at least 1 CSF diversion device. The anaerobic culture media was substituted at the time of case cluster. In 2021, the culture media was implemented permanently with no increase in C. acnes culture positivity. The rate of C. acnes positive CSF cultures and rate ratio increased in the outbreak period (p=0.01) compared to pre-outbreak and post-outbreak periods. There was no difference between the pre- and post-outbreak periods. Conclusions: Retrospective analysis of CSF culture data led to reclassifying a C. acnes pseudo-outbreak as a true outbreak in CSF diversion devices at our institution. Clearer guidance is needed to delineate the role of C. acnes in CSF diversion device infections.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".