Tuberculosis/cryptococcosis co-infection in China between 1965 and 2016
Bibliographic record
Abstract
Cases of tuberculosis/cryptococcosis co-infection are rapidly increasing in China. However, most studies addressing this co-infection have been published in Chinese journals, and this publication strategy has obscured this disease trend for scientists in other parts of the world. Our investigation found that 62.9% of all co-infection cases worldwide were reported in the Chinese population (n=197) between 1965 and 2016, and 56.3% of these Chinese cases were reported after 2010. Nearly all cases originated from the warm and wet monsoon regions of China. HIV-positive subjects tended to correlate with more severe manifestations of a tuberculosis/cryptococcosis co-infection than those without HIV. Notablely, dual tubercular/cryptococcal meningitis was the most frequent (54.0%) and most easily misdiagnosed (95.2%, n=40/42) co-infection. We also found that the combined use of cerebrospinal fluid pressure and concentrations of glucose, protein and chlorine might be an inexpensive and effective indicator to differentiate tubercular/cryptococcal co-infection meningitis from tubercular meningitis and cryptococcal meningitis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".