Risk factors for intradomiciliary infestation by the Chagas disease vector Triatoma dimidiatain Jutiapa, Guatemala
Bibliographic record
Abstract
Seventeen variables were evaluated as possible risk factors for the intradomiciliary infestation with Triatoma dimidiata in 644 houses in Jutiapa, Guatemala. During 2004 the houses were assessed for vector presence and evaluated for hygiene, cluttering, material comfort, construction conditions and number of inhabitants, among other factors. Chi-square analysis detected significant associations between vector presence and eight variables related to domestic sanitary and construction conditions. Log-linear models showed that regardless of the age of the house, the odds of vector presence were 4.3 and 10 times lower in houses with a good socioeconomic status compared with poor and very poor houses respectively. Log-linear models also pointed to a greater chance of vector presence when walls lacked plastering (3.85 times) or walls had low quality-incomplete plastering (4.56 times), compared with walls that were completely plastered. Control strategies against T. dimidiata should include the introduction of better-quality but inexpensive plastering formulations and better sanitation practices should also be promoted among the population. Such control strategies should not only reduce or eliminate infestation, but also prevent vector reinfestation.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".