Cryptococcosis in patients with diabetes mellitus <scp>II</scp> in mainland China: 1993‐2015
Bibliographic record
Abstract
Diabetes mellitus II (DM II) is a newly defined independent factor contributing to the morbidity and mortality of cryptococcosis. This retrospective case analysis aims to explore the epidemiology, clinical profile and strain characteristics of cryptococcosis in Chinese DM II patients. This study included 30 cases of cryptococcosis with DM II occurring from 1993 to 2015 in mainland China. The hospital-based prevalence of cryptococcosis in DM II was 0.21%. The mean age of the patients was 56.1 years (95% confidence interval: 51.5, 60.6), and 93% of the patients were older than 40 years. Sixty-two per cent of the patients experienced untreated or poorly controlled blood glucose before infection. Multilocus sequence typing analysis categorised all cultured strains as Cryptococcus neoformans and sequence type 5. Sixty-nine per cent of pulmonary cryptococcosis patients experienced misdiagnoses and treatment delays. Sixty per cent of cryptococcal meningitis patients received substandard antifungal therapy. The overall death rate was 33%. Considering the large population size of DM II patients in China, improved attention should be paid to the high prevalence of cryptococcosis as revealed by us. We also emphasised the importance of blood glucose control for infection prevention, especially among the elderly.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".