Group B Streptococcal Cellulitis and Necrotizing Fasciitis in Infants
Bibliographic record
Abstract
BACKGROUND: There is no consensus regarding approaches to infantile group B streptococcal (GBS) head and neck cellulitis and necrotizing fasciitis. We present a case of GBS necrotizing cellulitis and summarize the literature regarding the presentation and management of infantile head and neck GBS cellulitis and necrotizing fasciitis. METHODS: The literature was searched using PubMed, Web of Science, EMBASE and Medline (inception to April 2017) by 2 independent review authors. Inclusion criteria encompassed case reports or case series of infants less than 12 months of age with GBS cellulitis of the head and neck or with GBS necrotizing fasciitis without restriction to the head and neck. Data were extracted using tables developed a priori by 2 independent review authors, and discrepancies were resolved by consensus. RESULTS: An infant presenting at 33 days of age with GBS facial necrotizing fasciitis was successfully treated conservatively with antibiotics. Our literature search identified 40 infants with GBS head and neck cellulitis. Late-onset (98%), male gender (65%) and prematurity (58%) predominated. Penicillin is the main therapy used (97%). The 12 identified cases of necrotizing fasciitis were associated with polymicrobial etiology (36%) and broad-spectrum antibiotic use. Seventy-five percent required debridement, including 4 of 5 (80%) cases involving the head and neck. CONCLUSIONS: Skin and soft tissue involvement is an uncommon manifestation of late-onset GBS infection which requires antibiotic therapy and possibly surgical debridement cases with necrotizing fasciitis.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".