C-reactive protein and other biomarkers—the sense and non-sense of using inflammation biomarkers for the diagnosis of severe bacterial infection
Bibliographic record
Abstract
Severe bacterial infection (SBI) poses a significant clinical problem as its mortality and morbidity is still unacceptably high. A systematic literature analysis was performed with an emphasis on recent meta analyses examining the specificity and sensitivity of conventional inflammation biomarkers (C-reactive protein, procalcitonin, interleukin-6, interleukin-8) for diagnosing SBI. Most inflammation biomarkers do not show high sensitivity and are of limited value regarding SBI detection. To the practicing clinician, the sole use of inflammation markers is not useful for differentiating between viral or bacterial origin of infection in an individual patient. Thus, only in combination with clinical biometric markers, taken from patient history and physical examination, is the analysis of inflammation biomarkers to some degree helpful in clinical practice. To date, their sensitivity and specificity have been best captured in the field of neonatology, where levels of interleukin-6 have been measured in combination with relevant perinatal factors. The indiscriminate use of inflammation biomarkers for the diagnosis of SBI may lead to over diagnosis. Novel technologies for pathogen detection and more precise measurement of the host-response using microarrays, allowing for simultaneous detection of multiple genes or proteins, promise to improve the value of laboratory biomarkers for the diagnosis of SBI. Statement of novelty: Presented here is an up-to-date systematic analysis of C-reactive protein and inflammation biomarkers with regard to their use in the diagnosis of SBI. I question whether a broad use of C-reactive protein is useful in patients presenting with infection. The results of the systematic analysis are put into context with recent concerns about over-diagnosing in medicine. This paper is adapted from a publication in the German journal Monatsschrift Kinderheilkunde.
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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 | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".