Heparin-Binding Protein: A Diagnostic Biomarker of Urinary Tract Infection in Adults
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections (UTIs) are associated with significant morbidity and high frequency of antibiotic prescription. Diagnosing UTI is often difficult, particularly in the critically ill patient and in patients with unspecific and mild symptoms. The standard rapid tests have limited value, and there is a need for more reliable diagnostic tools. Heparin-binding protein (HBP) is released from neutrophils and has previously been studied as a diagnostic and predictive biomarker in different bacterial infections. METHODS: This prospective survey enrolled adult patients at 2 primary care units and 2 hospital emergency departments, to investigate in urine HBP as a biomarker of UTI. In addition, urine levels of interleukin-6, white blood cells, and nitrite were analyzed and compared with HBP. Based on symptoms of UTI and microbiological findings, patients were classified into different groups, UTI (cystitis and pyelonephritis) and no UTI. RESULTS: Three hundred ninety patients were evaluated. The prevalence of UTI in the study group was 45.4%. The sensitivity and specificity for HBP in urine as a marker for UTI were 89.2% and 89.8%, respectively. The positive and negative predictive values were 90.2% and 88.8%, respectively. Heparin-binding protein was the best diagnostic marker for UTI, with an area-under-curve value of 0.94 (95% confidence interval, 0.93-0.96). Heparin-binding protein was significantly better in distinguishing cystitis from pyelonephritis, compared with the other markers. CONCLUSIONS: An elevated level of HBP in the urine is associated with UTI and may be a useful diagnostic marker in adult patients with a suspected UTI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".