C-reactive protein for late-onset sepsis diagnosis in very low birth weight infants
Bibliographic record
Abstract
BACKGROUND: Late-onset sepsis in very low birth weight (VLBW) infants is a diagnostic challenge. We aimed to evaluate the diagnostic utility of the C-Reactive protein (CRP) and the complete blood count (CBC) for late-onset sepsis in VLBW infants. METHODS: , immature neutrophil/total neutrophil ratio > 0.10, or platelet count < 100,000/uL. Sensitivity and specificity for predicting late-onset sepsis were calculated for each laboratory test and their combinations. Receiver operating characteristics curves were obtained for each test and for the absolute change from T0 to T24 in the laboratory value of CRP, white blood cell count and immature neutrophil/total neutrophil. RESULTS: At T0, combining the CBC and the CRP had the highest sensitivity of 66% (95% confidence interval [CI], 58-73) compared to both individual tests for predicting late onset sepsis. At T24, CRP's sensitivity was 84% (95% CI, 78-89) and was statistically higher than the CBC's 59% (95% CI, 51-67). The combination of CBC at T0 and CRP at T24 offered the greatest sensitivity of 88% (95% CI, 82-92) and negative predictive value 93% (95% CI, 89-96), with fewer samples, compared to any other combination of tests. The area under the curve for the change in the white blood cell count from T0 to T24 was 0.82. CONCLUSION: At initial sepsis evaluation (T0), both CBC and CRP should be performed to increase sensitivity. A highly negative predictive value is reachable with only two tests: a CBC at T0 and a CRP a T24.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".