Procalcitonin Is a Specific Marker for Detecting Bacterial Infection in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is a chronic inflammatory disease accompanied by many complications, and serious infections are associated with many of the advanced therapeutics used to treat it. We assessed serum procalcitonin (PCT) levels to distinguish bacterial infection from other complications in patients with RA. METHODS: One hundred eighteen patients experiencing an RA flare, noninfectious complication of RA or its treatment, nonbacterial infection, or bacterial infection were studied. Serum PCT concentrations were determined with a chemiluminescent enzyme immunoassay. RESULTS: All patients experiencing an RA flare showed negative PCT levels (≤ 0.1 ng/ml; n = 18). The PCT level was higher in the bacterial infection group (25.8% had levels ≥ 0.5 ng/ml) than in the other 3 groups (0.0-4.3% had levels ≥ 0.5 ng/ml) and the difference was significant among groups (p = 0.003). Conversely, no statistically significant difference was observed among the groups with C-reactive protein (CRP) concentration ≥ 0.3 mg/dl (p = 0.513), white blood cell (WBC) count > 8500/mm(3) (p = 0.053), or erythrocyte sedimentation rate (ESR) > 15 mm/h (p = 0.328). The OR of high PCT level (≥ 0.5 ng/ml) for detection of bacterial infection was 19.13 (95% CI 2.44-149.78, p = 0.005). Specificity and positive likelihood ratio of PCT ≥ 0.5 ng/ml were highest (98.2% and 14.33, respectively) for detection of bacterial infection, although the sensitivity was low (25.8%). CONCLUSION: Serum PCT level is a more specific marker for detection of bacterial infection than either CRP, ESR, or WBC count in patients with RA. High PCT levels (≥ 0.5 ng/ml) strongly suggest bacterial infection. However, PCT < 0.5 ng/ml, even if < 0.2 ng/ml, does not rule out bacterial infection and physicians should treat appropriately.
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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 | 0.004 |
| 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 | 0.001 |
| 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".