Clinical and Cost-Effectiveness of Procalcitonin Test for Prodromal Meningococcal Disease–A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Despite vaccines and improved medical intensive care, clinicians must continue to be vigilant of possible Meningococcal Disease in children. The objective was to establish if the procalcitonin test was a cost-effective adjunct for prodromal Meningococcal Disease in children presenting at emergency department with fever without source. METHODS AND FINDINGS: Data to evaluate procalcitonin, C-reactive protein and white cell count tests as indicators of Meningococcal Disease were collected from six independent studies identified through a systematic literature search, applying PRISMA guidelines. The data included 881 children with fever without source in developed countries.The optimal cut-off value for the procalcitonin, C-reactive protein and white cell count tests, each as an indicator of Meningococcal Disease, was determined. Summary Receiver Operator Curve analysis determined the overall diagnostic performance of each test with 95% confidence intervals. A decision analytic model was designed to reflect realistic clinical pathways for a child presenting with fever without source by comparing two diagnostic strategies: standard testing using combined C-reactive protein and white cell count tests compared to standard testing plus procalcitonin test. The costs of each of the four diagnosis groups (true positive, false negative, true negative and false positive) were assessed from a National Health Service payer perspective. The procalcitonin test was more accurate (sensitivity=0.89, 95%CI=0.76-0.96; specificity=0.74, 95%CI=0.4-0.92) for early Meningococcal Disease compared to standard testing alone (sensitivity=0.47, 95%CI=0.32-0.62; specificity=0.8, 95% CI=0.64-0.9). Decision analytic model outcomes indicated that the incremental cost effectiveness ratio for the base case was £-8,137.25 (US $ -13,371.94) per correctly treated patient. CONCLUSIONS: Procalcitonin plus standard recommended tests, improved the discriminatory ability for fatal Meningococcal Disease and was more cost-effective; it was also a superior biomarker in infants. Further research is recommended for point-of-care procalcitonin testing and Markov modelling to incorporate cost per QALY with a life-time model.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.078 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".