RATE OF INFECTION (TUBERCULOSIS) IN BRAZILIANS IBD PRIVATE PATIENTS: FOLLOW-UP 15 YEARS
Bibliographic record
Abstract
BACKGROUND: Latent tuberculosis (LTB) is a condition where the patient is infected with Mycobacterium tuberculosis but does not develop active TB. There's a possibility of tuberculosis (TB) activation following the introduction of anti-TNFs. OBJECTIVE: To assess the risk of biological therapy inducing LTB during inflammatory bowel diseases (IBD) treatment over 15 years in a high-risk area in Brazil. METHODS: A retrospective study of an IBD patients' database was carried out in a private reference clinic in Brazil. All patients underwent TST testing and chest X-ray prior to treatment, and once a year after starting it. Patients were classified according to the Montreal stratification and risk factors were considered for developing TB. RESULTS: Among the analyzed factors, age and gender were risk factors for LTB. DC (B2 and P) and UC (E2) patients showed a higher number of LTB cases with statistical significance, what was also observed for adalimumab and infliximab users, compared to other medications, and time of exposure to them favored it significantly. Other factors such as enclosed working environment have been reported as risk. CONCLUSION: The risk of biological therapy causing LTB is real, so patients with IBD should be continually monitored. This study reveals that the longer the exposure to anti-TNFs, the greater the risk. BACKGROUND: •Rate of infection (tuberculosis) in Brazilians IBD private patients: follow-up 15 years. BACKGROUND: •Patients treated with immunosuppressants and/or anti-TNFs have a higher risk of developing opportunistic infections, among them the most common is latent tuberculosis or even active tuberculosis. BACKGROUND: •Similar risks may be noted in patients with inflammatory bowel diseases (IBDs). BACKGROUND: •This study reveals that the longer the exposure to anti-TNFs, the greater the risk for de IBD patients. BACKGROUND: •The study demonstrated the importance of monitoring these patients permanently and continuously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".