Contribution of immunodiagnostic tests to epidemiological/intervention studies of cysticercosis/taeniosis in México
Bibliographic record
Abstract
Cysticercosis is acquired when swine ingest human faeces contaminated with Taenia solium eggs. Humans become tapeworm carriers when they ingest infected pork meat. They can also develop cysticercosis after inadvertently swallowing T. solium eggs. Human neurocysticercosis (NCC) is considered as a public health problem in Mexico and in several countries around the world, mainly developing ones. The development of immunodiagnostic techniques has promoted the conduct of seroepidemiological studies. This review provides insight into the evolution of these techniques, their predictive values and their use in field studies, and summarizes evidence supporting health care practice and policy related to cysticercosis/taeniosis in Mexico. Serological studies in rural and urban settings have demonstrated that close proximity with a tapeworm carrier is the main risk factor for acquiring cysticercosis. Research focusing on the tapeworm carrier generated an ELISA for the detection of Taenia coproantigens and facilitated the evaluation of intervention measures. Health education and self-identification of tapeworm carriers were shown to be successful. However, cestodial treatment as a community-based intervention was not as successful. Current immunodiagnostic techniques can be used to pinpoint transmission foci so that appropriate and effective interventions can be applied. In this way, sustainable control, and even eradication of T. solium may be envisioned.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".