Toxoplasmosis Behind Bars: One Health Approach on Serosurvey Dynamics and Associated Risk Factors for Women Inmates, Correctional Officers, and In‐Prison Feral Cats
Bibliographic record
Abstract
Brazil holds the third highest general and fifth female incarcerated population worldwide. Despite the incarceration ecosystem that may favor the spreading of zoonotic diseases, particularly when unattended animals are present, no comprehensive study has focused on toxoplasmosis dynamics in such environment. Accordingly, the present study has aimed to serologically assess anti‐Toxoplasma gondii (IgG) antibodies by indirect immunofluorescent antibody test in inmates, correctional officers, and feral cats at the Women’s State Penitentiary of Parana, southern Brazil. In overall, 230/506 (45.5%; CI 95%: 41.2–49.8) incarcerated women, 31/91 (34.1%; 95% CI: 25.2–44.3) correctional officers, and 23/39 (59.0%; CI 95%: 43.2–72.9) cats were seropositive to anti‐T. gondii antibodies. Logistic regression revealed that seropositivity likelihood increased with consumption of raw meat (p = 0.040) and decreased with elementary educational level (p = 0.001). No statistical difference was found comparing seropositivity between inmates and correctional officers (p = 0.057). As women inmates have been considered among the most vulnerable groups in disease morbidity and mortality, seropositivity observed herein may be directly related to vulnerability and high T. gondii oocyst exposure dispersed in cat feces during incarceration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".