Tumor necrosis factor α level in cerebrospinal fluid for bacterial and aseptic meningitis: a diagnostic meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In our previous study, tumor necrosis factor α (TNF-α) was identified as an effective target for sepsis patients (Int J Clin Pract, 68, 2014, 520). TNF-α in cerebrospinal fluid (CSF) was also investigated for its utility in the differential diagnosis of bacterial and aseptic meningitis. However, there has been neither definite nor convincing evidence so far. Here the overall diagnostic accuracy of TNF-α in differentiation between bacterial and aseptic meningitis was evaluated through the meta-analysis of diagnostic tests. METHODS: The sensitivity, specificity and other measures of accuracy were pooled using random effect models. Summary receiver operating characteristic curves were used to assess overall test performance. Publication bias was evaluated using funnel plots, and sensitivity analysis was also introduced. RESULTS: A total of 21 studies involving bacterial meningitis (678) and aseptic meningitis (694) involved a total of 1372 patients. The pooled sensitivity and specificity for the TNF-α test were 0.83 [95% confidence interval (CI) 0.80-0.86, I(2) = 65.1] and 0.92 (95% CI 0.89-0.94, I(2) = 61.8), respectively. The positive likelihood ratio was 12.05 (95% CI 7.41-19.60, I(2) = 36.5), the negative likelihood ratio was 0.17 (95% CI 0.13-0.24, I(2) = 59.4), and TNF-α was significantly associated with bacterial meningitis, with a diagnostic odds ratio of 49.84 (95% CI 28.53-87.06, I(2) = 47.9). The overall accuracy of the TNF-α test was very high with the area under the curve 0.9317. Publication bias was absent, and sensitivity analysis suggested that our results were highly stable. CONCLUSIONS: Our meta-analysis suggested that TNF-α could be recommended as a useful marker for diagnosis of bacterial meningitis and differential diagnosis between bacterial and aseptic meningitis with high sensitivity and specificity. Thus, hospitals should be encouraged to conduct TNF-α tests in CSF after lumbar puncture.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".