Biofire FilmArray Meningitis/Encephalitis panel for the aetiological diagnosis of central nervous system infections: A systematic review and diagnostic test accuracy meta-analysis
Bibliographic record
Abstract
BACKGROUND: The FilmArray Meningitis/Encephalitis(FA/ME) panel brings benefits in clinical practice, but its diagnostic test accuracy (DTA) remains unclear. We aimed to determine the DTA of FA/ME for the aetiological diagnostic in patients with suspected central nervous system(CNS) infection. METHODS: We performed a systematic review with DTA meta-analysis (PROSPERO: CRD42020139285). We searched Embase, Medline (Ovid), and Web of Science from inception until September 1st, 2021. We assessed the study-level risk of bias with the QUADAS-2 tool and applied the GRADE approach to assess the certainty of the synthesised evidence. We included studies that simultaneously measured the reference test (CSF/blood culture for bacteria, and specific polymerase chain reaction for viruses) and the FA/ME in patients with suspected CNS infection. We performed random-effects bivariate meta-analysis models of combined sensitivity and specificity using CSF/blood cultures(reference test 1) and a final diagnosis adjudication based on clinical/laboratory criteria (reference test 2). FINDINGS: We included 19 studies (11,351 participants). For all bacteria with reference test 1 (16 studies/6183 patients) sensitivity was estimated at 89·5% (95%CI 81·1-94·4), and specificity at 97·4% (95%CI 94-98·9). With reference test 2 (15 studies/5,524 patients), sensitivity was estimated at 92·1%(95%CI 86·8-95·3) and specificity at 99.2(95%CI 98·3-99·6) For herpes simplex virus-2(HSV-2), enteroviruses, and Varicella-Zoster virus (VZV), we obtained sensitivities between 75·5 and 93·8%, and specificities above 99% (reference test 1). Certainty of the evidence was low. INTERPRETATION: , and HSV-1 were suboptimal. FUNDING: None.
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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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".