Challenges in Diagnosis of Congenital Toxoplasmosis on Postimplementation of Minas Gerais Screening Program
Bibliographic record
Abstract
BACKGROUND: Congenital toxoplasmosis is both prevalent and severe in Brazil. The Minas Gerais Congenital Toxoplasmosis Control Program (PCTC-MG) used prenatal and neonatal screening to identify neonates at risk for congenital toxoplasmosis. This study aimed to evaluate the clinical and laboratory parameters used to diagnose the disease in this population. METHODS: This retrospective cohort study included children with suspected congenital toxoplasmosis who participated in the PCTC-MG between 2013 and 2020. RESULTS: A total of 347 children participated in the study; 228 had confirmed toxoplasmosis and 119 were excluded. The majority (314/347; 90.5%) underwent neonatal screening for IgM in filter paper (FP). Among these, 269/314 (85.7%) had positive or indeterminate results, with 186 (69.1%) confirmed infections, while 45/314 (14.3%) had nonreactive results, with 17 confirmed infections. There was an association between treatment during pregnancy (45/227; 19.8%) and a lower number of reagent IgM results in FP ( P = 0.002) and serum ( P = 0.001). A higher gestational age was associated with a higher proportion of IgM in the FP ( P = 0.001) and serum ( P = 0.004). Retinochoroiditis (73.2%; 167/228) and neurologic changes (36.9%; 75/203) were frequent in the infected children. The treatment decision was based on the presence of IgM/IgA (176/226; 77.9%), retinochoroiditis (45/226; 19.9%) or persistence/increase in IgG levels (4/226; 1.8%). CONCLUSIONS: Screening with specific and sensitive serology identified most, but not all, children with congenital toxoplasmosis. Ophthalmologic evaluations and neuroimaging are mandatory in this context. The absence of IgM in the FP did not exclude the diagnosis.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".