Infection biomarkers in primary care patients with acute respiratory tract infections–comparison of Procalcitonin and C-reactive protein
Bibliographic record
Abstract
BACKGROUND: There is a lack of studies comparing the utility of C-reactive protein (CRP) with Procalcitonin (PCT) for the management of patients with acute respiratory tract infections (ARI) in primary care. Our aim was to study the correlation between these markers and to compare their predictive accuracy in regard to clinical outcome prediction. METHODS: This is a secondary analysis using clinical and biomarker data of 458 primary care patients with pneumonic and non-pneumonic ARI. We used correlation statistics (spearman's rank test) and multivariable regression models to assess association of markers with adverse outcome, namely days with restricted activities and persistence of discomfort from infection at day 14. RESULTS: At baseline, CRP and PCT did not correlate well in the overall population (r(2) = 0.16) and particularly in the subgroup of patients with non-pneumonic ARI (r(2) = 0.08). Low correlation of biomarkers were also found when comparing cut-off ranges, day seven levels or changes from baseline to day seven. High baseline levels of CRP (>100 mg/dL, regression coefficient 1.6, 95 % CI 0.5 to 2.6, sociodemographic-adjusted model) as well as PCT (>0.5ug/L regression coefficient 2.0, 95 % CI 0.0 to 4.0, sociodemographic-adjusted model) were significantly associated with larger number of days with restricted activities. There were no associations of either biomarker with persistence of discomfort at day 14. CONCLUSIONS: CRP and PCT levels do not well correlate, but both have moderate prognostic accuracy in primary care patients with ARI to predict clinical outcomes. The low correlation between the two biomarkers calls for interventional research comparing these markers head to head in regard to their ability to guide antibiotic decisions. TRIAL REGISTRATION: Current Controlled Trials, ISRCTN73182671.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".