Mortality in Patients with Necrotizing Fasciitis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The prognostic factors that determine outcome in patients with necrotizing fasciitis remain poorly understood. The aim of this study was to analyze the variables that affect the mortality and morbidity of patients with necrotizing fasciitis and to create a simple method for estimating the probability of mortality. METHODS: The authors undertook a retrospective review of all patients with necrotizing fasciitis treated in three tertiary care hospitals in Ontario, Canada, between January of 1994 and June of 2001. Demographic, comorbid illness, and disease-specific data were collated and analyzed for associations with outcome. Using logistic regression analysis, probability estimates for the prediction of mortality were developed, based on three contributing independent factors. RESULTS: Ninety-nine patients satisfied the inclusion criteria. Overall mortality was 20 percent. Sixteen patients suffered from amputation or organ loss. The most common comorbidities were diabetes (30 percent), immunocompromised status (17 percent), and chickenpox (11 percent). Advanced age (odds ratio, 1.04; 95 percent confidence interval, 1.01 to 1.08; p = 0.012), streptococcal toxic shock syndrome (odds ratio, 10.54; 95 percent confidence interval, 2.80 to 39.44; p < 0.001), and immunocompromised status (odds ratio, 3.97; 95 percent confidence interval, 1.04 to 15.19; p = 0.044) were independent predictors of mortality and were used to design a formula for the probability of mortality. CONCLUSIONS: Age, streptococcal toxic shock syndrome, and immune status are significant determinants of mortality and can predict the probability of death from necrotizing fasciitis soon after admission. This objective information can guide clinicians in communication with patients and in making clinical decisions.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it