<i>Ascaris lumbricoides</i>: A Review of Its Epidemiology and Relationship to Other Infections
Bibliographic record
Abstract
This review highlights advances made since 2004 in understanding the epidemiology of infection and the interactions between <i>Ascaris</i><i>lumbricoides</i> and other concurrent infections. As water scarcity increases, untreated wastewater is increasingly used to irrigate crops, thus increasing the risk of transmission. New methods to detect and inactivate <i>Ascaris</i> eggs in water, soil and food are described. The association between pig ownership and <i>Ascaris</i> infection in humans may represent cross-transmission as hybridization among the pig and human ascarids occurs more frequently than previously believed. Geospatial analyses have successfully predicted infection levels both at a regional level (based on vegetation indices, temperature and humidity) and within communities (based on social and environmental factors). The interpretation of antibody and cytokine responses to <i>Ascaris</i> is becoming clearer, as researchers recognize the role of antigen type, age, the history of <i>Ascaris</i> and other infections. The considerable interest emerging on the interactions between <i>Ascaris</i> and other infections (helminths, malaria, HIV, tuberculosis) and allergy is explored. The impact of concurrent infection on the design of control strategies is discussed including the benefits arising from combination therapies and the evidence that intestinal nematodes impair the efficacy of childhood vaccines. Finally, recommended areas for future research are identified.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".