Diagnostic Value of PCT and CRP for Detecting Serious Bacterial Infections in Patients With Fever of Unknown Origin: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is vital to recognize the cause of an infection to enable earlier treatment. Studies have shown that procalcitonin (PCT) and C-reactive protein (CRP) have very high sensitivity and specificity for diagnosing serious bacterial infections (SBIs), with PCT performing better than CRP. METHODS: Multiple databases were searched for relevant studies, and full-text articles involving diagnosis with PCT and CRP were reviewed. All meta-analyses were conducted with Review Manager 5.0. Sensitivity and bias analyses were performed to evaluate the quality of articles. In addition, a funnel plot and Egger test were used to assess possible publication bias. RESULT: A total of 17 articles met the criteria for inclusion. The concentrations of both PCT and CRP were higher in the SBI group than in the nonbacterial infection group. Sensitivity for differentiating bacterial infections from nonbacterial infections was higher for PCT compared with CRP, whereas there was no significant difference in specificity. The area under the summary receiver operating characteristic curve for PCT was larger than that for CRP. CONCLUSION: Both PCT and CRP are useful markers and should be used to evaluate SBIs with fever of unknown origin.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".