Alveolar and Serum Procalcitonin
Bibliographic record
Abstract
BACKGROUND: The potential role of serum and alveolar procalcitonin as early markers of ventilator-associated pneumonia (VAP) and its prognostic value were investigated. METHODS: Ninety-six patients with a strong suspicion of VAP were prospectively enrolled. VAP diagnosis was based on a positive quantitative culture obtained via a mini-bronchoalveolar lavage of 103 colony-forming units/ml or more. Blood and alveolar samples were collected for procalcitonin measurement and analyzed for diagnostic and prognostic evaluation on days 0, 3, and 6. Sensitivity, specificity, positive likelihood ratio, and receiver-operating characteristic curves were analyzed to define ideal cutoff values and approach the decision analysis. RESULTS: Serum procalcitonin was significantly increased in the VAP group (n = 44) compared with the non-VAP group (n = 52): 11.5 ng/ml (95% confidence interval, 5.9-17.0) versus 1.5 ng/ml (1.1-1.9). A serum procalcitonin concentration greater than 3.9 ng/ml (best cutoff value) was considered positive for the VAP diagnosis (sensitivity, 41%; specificity, 100%). Serum procalcitonin was significantly increased in the non-survivors compared with the survivors for the VAP group: 16.5 ng/ml (95% confidence interval, 8.1-24.9) versus 2.9 ng/ml (1.2-4.7). The best cutoff value for serum procalcitonin of the nonsurvivors in the VAP group was 2.6 ng/ml (sensitivity, 74%; specificity, 75%; positive likelihood ratio, 2.96). Regarding VAP diagnosis and prognosis, no significant differences were found for alveolar procalcitonin in all groups. CONCLUSIONS: Serum but not alveolar procalcitonin seems to be a helpful parameter in the early VAP diagnosis and an appropriate marker for predicting mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".