Analysis of prognostic factors affecting mortality in Fournier’s gangrene: A study of 72 cases
Bibliographic record
Abstract
INTRODUCTION: Fournier's gangrene is a rapidly progressing necrotizing fasciitis of the perineum and genital area associated with a high mortality rate. We presented our experience in managing this entity and identified prognostic factors affecting mortality. METHODS: We carried out a retrospective study of 72 patients treated for Fournier's gangrene at our institution between January 2005 and December 2014. Patients were divided into survivors and non-survivors and potential prognostic factors were analyzed. RESULTS: Of the 72 patients, 64 were males (89%) and 8 females (11%), with a mean age of 51 years. The most common predisposing factor was diabetes mellitus (38%). The mortality rate was 17% (12 patients died). Statistically significant differences were not found in age, gender, and predisposing factors, except in heart disease (p = 0.038). Individual laboratory parameters significantly correlating with mortality included hemoglobin (p = 0.023), hematocrit (p = 0.019), serum urea (p = 0.009), creatinine (p = 0.042), and potassium (p = 0.026). Severe sepsis on admission and the extent of affected surface area also predicted higher mortality. Others factors, such as duration of symptoms before admission, number of surgical debridement, diverting colostomy and length of hospital stay, did not show significant differences. The median Fournier's Gangrene Severity Index (FGSI) was significantly higher in non-survivors (p = 0.002). CONCLUSION: Fournier's gangrene is a severe surgical emergency requiring early diagnosis and aggressive therapy. Identification of prognostic factors is essential to establish an optimal treatment and to improve outcome. The FGSI is a simple and valid method for predicting disease severity and patient survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".