Necrotizing fasciitis secondary to group A streptococcus. Morbidity and mortality still high.
Bibliographic record
Abstract
OBJECTIVE: To update physicians on Group A streptococcal necrotizing fasciitis, including current methods of diagnosis and treatment. QUALITY OF EVIDENCE: Current literature (1990-1998) was searched via MEDLINE using the MeSH headings necrotizing fasciitis, toxic shock syndrome, and Streptococcus. Articles were selected based on clinical relevance and design. Most were case reports, case series, or population-based surveys. There were no randomized controlled trials. MAIN MESSAGE: The hallmark of clinical diagnosis of necrotizing fasciitis is pain out of proportion to physical findings. Suspicion of underlying soft tissue infection should prompt urgent surgical examination. Therapy consists of definitive excisional surgical debridement in conjunction with high-dose intravenous penicillin G and clindamicin. Risk factors for mortality include advanced age, underlying illness, hypotension, and bacteremia. CONCLUSION: Necrotizing soft tissue infections due to Group A streptococcus might be increasing in frequency and aggression. Overall mortality remains high (20% to 34% in larger series). Clinical diagnosis requires a high level of suspicion and should prompt urgent surgical referral.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".