Risk Factors for Hospitalization With Lower Respiratory Tract Infections in Children in Rural Alaska
Bibliographic record
Abstract
OBJECTIVE: Lower respiratory tract infections (LRTIs) are a major cause of morbidity for children worldwide and particularly for children from developing and indigenous populations. In this study, we evaluated risk factors for hospitalization with LRTI in a region in southwest Alaska. METHODS: The study was conducted from October 1, 2006, to September 30, 2007, in the Yukon Kuskokwim Delta region of Alaska. Cases were recruited from children <3 years of age hospitalized with LRTI. Controls were recruited during visits to the surrounding communities in the region and matched posthoc to cases on the basis of subregion, season, and age. Parents were interviewed for potential risk factors, and medical records were reviewed. Participants had a nasopharyngeal swab sample taken for polymerase chain reaction (PCR) testing for a panel of respiratory viruses. Samples positive for respiratory syncytial virus, human metapneumovirus, or parainfluenza type 3 were quantitated by reverse transcriptase real-time quantitative PCR. RESULTS: One hundred twenty-eight cases were matched to 186 controls. In a multivariable conditional logistic regression model, significantly (P < .05) increased risk of hospitalization was associated with medically high-risk status, having a woodstove in the house, being bottle fed, and vomiting after feeding; living in a house that had 2 or more rooms with sinks was a protective factor. Viral loads in hospitalized cases were significantly higher than those in controls, but a strict cutoff level was not observed. CONCLUSIONS: Several risk factors for LRTI hospitalization were identified in this high risk population. Some factors are amenable to environmental and behavioral interventions.
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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 | 0.000 |
| 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) | 0.002 | 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".