Beyond the Complete Blood Cell Count and C-Reactive Protein
Bibliographic record
Abstract
OBJECTIVE: To systematically review the accuracy of modern laboratory tests for the diagnosis of serious bacterial infection in newborns. METHODS: The MEDLINE, EMBASE, and Cochrane Library databases were searched using the keywords newborn, infection, sepsis, and diagnosis. We included studies published from 1995 through 2001 that included infants younger than 90 days with proven bacterial growth in a sample from a sterile site. Whenever possible, relevant data were extracted to calculate likelihood ratios (LRs) for whether each test can diagnose a serious bacterial infection. Two independent reviewers selected and reviewed the articles (interobserver agreement, kappa = 0.80). All disagreements were resolved by consensus. RESULTS: Of the 137 citations we retrieved, 37 articles met the inclusion criteria; 17 studies, evaluating 11 different tests, met the highest methodological criteria. The most commonly evaluated test was interleukin 6 (IL-6) level (n = 7 studies). The remaining tests were each evaluated in no more than 3 studies. Positive LRs ranged from 1.5 to infinity. Six individual tests examined in 8 studies had LRs of more than 10 (range, 12.5- infinity ). Combined tests also had a wide range of LRs (3.4-9.9). All studies were performed in single medical centers and had small sample sizes, making recommendations according to gestational age criteria difficult. CONCLUSIONS: We found few methodologically rigorous studies of the accuracy of laboratory tests for the diagnosis of bacterial infection in newborns; in a significant proportion of studies, the accuracy of the tests could not be independently determined because of a lack of adequate data. There was marked heterogeneity in sample selection and cutoff levels for diagnosis of neonatal sepsis. A few tests showed promising accuracy, but there are insufficient data to support their confident use as clinical tools.
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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 | 0.026 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".