Diagnosis and treatment of tuberculosis presenting as uveitis based on stochastic simulation in systems biology
Bibliographic record
Abstract
Tuberculous uveitis can be a manifestation of extrapulmonary tuberculosis or an allergic reaction to tuberculosis infection. The clinical signs and symptoms of other uveitis causes are generic, making a false diagnosis simple. We present a brief introduction to theoretical modelling and simulation in systems biology and explore the consequences of TB uveitis if left untreated. Patients were admitted to our hospital with recurrent fever. They had a previous definitive diagnosis of binocular uveitis and a positive interferon gamma release assay (IGRA) test result. At the time, there was no antituberculosis medicine available, and immunosuppressive and glucocorticoid therapy did not work. After the admission, their pleural fluid tested positive for Mycobacterium tuberculosis. No other causes to explain the fever were found. A diagnosis of tuberculosis was made, and their body temperature normalized after antituberculosis treatment and closed chest drainage. Vigilance should be exercised to rule out tuberculous uveitis in cases of unexplained uveitis; this calls for IGRA screening, tuberculin skin testing, and cyst imaging. For patients with latent tuberculosis infections, it is recommended to administer antituberculosis treatment, after excluding other possible causes, and to avoid using glucocorticoids in isolation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".