Evaluation of a multiplex polymerase chain reaction for early diagnosis of ventriculostomy-related infections
Bibliographic record
Abstract
OBJECT: Diagnosis of ventriculostomy-related infections (VRIs) is challenging due to the lack of rapid, sensitive assays for pathogen detection. The authors report the development of a multiplex polymerase chain reaction (PCR) assay for differential diagnosis of common VRI pathogens. METHODS: MassTag PCR was used to develop a multiplex assay for detection of 11 VRI pathogens. The assay was established and optimized using cloned template standards and spiked samples and was then evaluated on CSF specimens from ventricular drains. Subjects were grouped into definite VRI, possible VRI, or no VRI based on conventional microbiology, CSF evaluation, and clinical parameters. RESULTS: CSF specimens were obtained from 45 subjects (median age 49 years, interquartile range 32-63 years; 51% were male). The assay detected 10-100 genome copies. It detected a pathogen in 100% (6 of 6) of definite VRI cases in which a pathogen targeted by the assay was present; these represented 67% of all definite VRIs (6 of 9). Among subjects with a possible VRI, the assay detected a pathogen in 29% (5 of 17). In subjects without overt infection the presence of a pathogen was detected in 32% of subjects (6 of 19), albeit with lower signal compared with the VRI group. CONCLUSIONS: MassTag PCR enabled parallel testing of CSF specimens for 11 pathogens of VRI. The high sensitivity of PCR combined with possible device colonization, specimen contamination, and concurrent antibiotic treatments limit the clinical value of the assay, similar to other current diagnostic approaches. With further optimization, multiplex PCR may provide timely identification of multiple possible VRI pathogens and guide management, complementing classic culture approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".