Stratification of Culture-Proven Early-Onset Sepsis Cases by the Neonatal Early-Onset Sepsis Calculator: An Individual Patient Data Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To provide a comprehensive assessment of case stratification by the Neonatal Early-Onset Sepsis (EOS) Calculator, a novel tool for reducing unnecessary antibiotic treatment. STUDY DESIGN: A systematic review with individual patient data meta-analysis was conducted, extending PROSPERO record CRD42018116188. Cochrane, PubMed/MEDLINE, EMBASE, Web of Science, Google Scholar, and major conference proceedings were searched from 2011 through May 1, 2020. Original data studies including culture-proven EOS case(s) with EOS Calculator application, independent from EOS Calculator development, and including representative birth cohorts were included. Relevant (individual patient) data were extracted from full-text and data queries. The main outcomes were the proportions of EOS cases assigned to risk categories by the EOS Calculator at initial assessment and within 12 hours. Evidence quality was assessed using Newcastle-Ottawa scale, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies, and GRADE tools. RESULTS: Among 543 unique search results, 18 were included, totaling more than 459 000 newborns. Among 234 EOS cases, EOS Calculator application resulted in initial assignments to (strong consideration of) empiric antibiotic administration for 95 (40.6%; 95% CI, 34.2%-47.2%), more frequent vital signs for 36 (15.4%; 95% CI, 11.0%-20.7%), and routine care for 103 (44.0%; 95% CI, 37.6%-50.6%). By 12 hours of age, these proportions changed to 143 (61.1%; 95% CI, 54.5%-67.4%), 26 (11.1%; 95% CI, 7.4%-15.9%), and 65 (27.8%; 95% CI, 22.1%-34.0%) of 234 EOS cases, respectively. CONCLUSIONS: EOS Calculator application assigns frequent vital signs or routine care to a substantial proportion of EOS cases. Clinical vigilance remains essential for all newborns.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".