73: Comparison of a Sepsis-Risk Calculator to Clinical Algorithm Used to Screen for Early-Onset Sepsis
Bibliographic record
Abstract
The sepsis risk calculator designed by Puopolo et al (Pediatrics 2011) estimates early onset neonatal sepsis (EOS) risk based on intrapartum factors, and has resulted in the safe evaluation of significantly fewer infants. This group has also provided further management strategies based on the clinical condition of the newborn. To compare the risk estimate for EOS calculated using the newly published sepsis risk calculator of Puopolo et al to the standard clinical algorithm recommended by the Canadian Pediatric Society (CPS) and to find its utility within a local population. All newborns (>36 weeks of gestation) born at a Canadian tertiary center between February 2014 and July 2014 were included. Infants born to mothers with inadequate GBS prophylaxis, chorioamnionitis or unknown GBS status with risk factors were screened and sepsis risk was estimated using a computerized calculator based on maternal predictors. These infants were followed to determine their management strategies based on clinical conditions. 971 infants were born during the study period. Based on the CPS algorithm, 66 (6.7%) infants were evaluated. Demographic data are available in Table 1. There was no culture positive sepsis in the study group. The sepsis risk calculator identified only two infants (3.03%) with a risk of >0.5/1000. When clinical variables were entered, antibiotic treatment was suggested for 7/66 (10.6%). Current strategies to reduce EOS based on preventive measures have resulted in a decrease in the risk of sepsis. Application of the sepsis risk calculator to our population truly depicts its strength but larger studies are needed to find the true incidence of EOS in the targeted population and to further evaluate the practical impact of this calculator on EOS identification.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".