Diabetes mellitus as the major risk factor for mucormycosis in Mexico: Epidemiology, diagnosis, and outcomes of reported cases
Bibliographic record
Abstract
Mucormycosis is an emerging infectious disease with high rates of associated mortality and morbidity. Little is known about the characteristics of mucormycosis or entomophthoromycosis occurring in Mexico. A search strategy was performed of literature published in journals found in available databases and theses published online at Universidad Nacional Autónoma de México (UNAM) library website reporting clinical cases or clinical case series of mucormycosis and entomophthoromycosis occurring in Mexico between 1982 and 2016. Among the 418 cases identified, 72% were diabetic patients, and sinusitis accounted for 75% of the reported cases. Diabetes mellitus was not a risk factor for entomophthoromycosis. Mortality rate was 51% (125/244). Rhizopus species were the most frequent isolates (59%, 148/250). Amphotericin B deoxycholate was used in 89% of cases (204/227), while surgery and antifungal management as combined treatment was used in 90% (172/191). In diabetic individuals, this combined treatment approach was associated with a higher probability of survival (95% vs 66%, OR = 0.1, 95% CI, 0.02-0.43' P = .002). The most common complications were associated with nephrotoxicity and prolonged hospitalization due to IV antifungal therapy. An algorithm is proposed to establish an early diagnosis of rhino-orbital cerebral (ROC) mucormycosis based on standardized identification of warning signs and symptoms and performing an early direct microbiological exam and histopathological identification through a multidisciplinary medical and surgical team. In summary, diabetes mellitus was the most common risk factor for mucormycosis in Mexico; combined antifungal therapy and surgery in ROC mucormycosis significantly improved 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".