Risk Factors for Relapse of Human Brucellosis
Bibliographic record
Abstract
BACKGROUND & PROPOSE: Brucellosis is serious disease around the world, especially in underdeveloped countries. Relapse is major problem in therapy of brucellosis. This study aimed to evaluate risk factors of relapse after treatment in patients. METHODS: It is a descriptive-analytic study from 1990 to 2014, in Ayatolla Rohani hospital in Babol, Iran. We studied 980 patients with brucellosis. The studied community included patients infected with brucellosis and the required information was gathered based on their hospital files. The base for recognizing Malta fever were clinical symptoms and Para-clinical sign congruent with infection like as, titer SAT>1:320 and 2-ME>1:160. Patients with relapse and patients without relapse were placed separately in two groups. The data were statistically compared with Spss 16, by Chi-square and Cox -regression tests. RESULTS: Based on this study, treatment regimen is a preventive factor (P=0.000). Moreover, Based on some statistical methods, regimens no. 3 and 4 were introduce preventive factors (P=0.001) and (P=0.004). It should also be noted that findings the same statistical model, factors like gender, age, residence, professional contacts, complications and delay in treatment were also analyzed but none of them are considered as preventive factors. CONCLUSION: Based our finding, we suggest aminoglycosides (gentamicin or streptomycin with doxycycline) are associated with lower rate of relapse in brucellosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".