Neonatal Mastitis and Concurrent Serious Bacterial Infection
Bibliographic record
Abstract
OBJECTIVES Describe the clinical presentation, prevalence, and outcomes of concurrent serious bacterial infection (SBI) among infants with mastitis. METHODS Within the Pediatric Emergency Medicine Collaborative Research Committee, 28 sites reviewed records of infants aged ≤90 days with mastitis who were seen in the emergency department between January 1, 2008, and December 31, 2017. Demographic, clinical, laboratory, treatment, and outcome data were summarized. RESULTS Among 657 infants (median age 21 days), 641 (98%) were well appearing, 138 (21%) had history of fever at home or in the emergency department, and 63 (10%) had reported fussiness or poor feeding. Blood, urine, and cerebrospinal fluid cultures were collected in 581 (88%), 274 (42%), and 216 (33%) infants, respectively. Pathogens grew in 0.3% (95% confidence interval [CI] 0.04–1.2) of blood, 1.1% (95% CI 0.2–3.2) of urine, and 0.4% (95% CI 0.01–2.5) of cerebrospinal fluid cultures. Cultures from the site of infection were obtained in 335 (51%) infants, with 77% (95% CI 72–81) growing a pathogen, most commonly methicillin-resistant Staphylococcus aureus (54%), followed by methicillin-susceptible S aureus (29%), and unspecified S aureus (8%). A total of 591 (90%) infants were admitted to the hospital, with 22 (3.7%) admitted to an ICU. Overall, 10 (1.5% [95% CI 0.7–2.8]) had sepsis or shock, and 2 (0.3% [95% CI 0.04–1.1]) had severe cellulitis or necrotizing soft tissue infection. None received vasopressors or endotracheal intubation. There were no deaths. CONCLUSIONS In this multicenter cohort, mild localized disease was typical of neonatal mastitis. SBI and adverse outcomes were rare. Evaluation for SBI is likely unnecessary in most afebrile, well-appearing infants with mastitis.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".