Lack of an Adverse Effect of Giardia intestinalis Infection on the Health of Peruvian Children
Bibliographic record
Abstract
Giardia intestinalis is a common gastrointestinal protozoan worldwide, but its effects on childhood growth in developing countries are not clearly understood. The authors aimed to describe its effects on child growth. They followed 220 Peruvian children daily for diarrhea, weekly for stool samples, and monthly for anthropometry. The authors modeled the effect of nutritional status on the risk of Giardia infection and the risk of diarrhea attributable to Giardia using negative binomial regression. They modeled the effects of Giardia infection on growth using linear regression, with 85% of children becoming infected with Giardia and 87% of these becoming reinfected. In multivariable analysis, the risk of Giardia infection did not vary with weight for age (relative risk = 1.00, 95% confidence interval: 0.89, 1.12) or height for age (relative risk = 0.92, 95% confidence interval: 0.82, 1.04). Giardiasis did not affect growth at 1 or 2 months following the first infection at any age interval. The longitudinal prevalence of Giardia between 6 and 24 months of age was not associated with height gain in that interval (p = 0.981). Giardia was not associated with an increased risk of diarrhea at any age interval. Study results question the importance of Giardia as a childhood pathogen in developing countries where giardiasis is hyperendemic.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".