Predictors of hospitalization for lower respiratory tract infection in children aged <2 years in the province of Quebec, Canada
Bibliographic record
Abstract
Young age, adverse environmental conditions and infectious agents are established risk factors of lower respiratory tract infection (LRTI), whereas pneumococcal conjugate vaccines may be protective. To explore their relative role as predictors of hospitalizations under the continental climate prevailing in the province of Quebec, Canada, an ecological study was performed. Records with a main diagnosis of LRTI in children born during 2007-2010 and observed up to their second-year anniversary were extracted from the provincial hospital administrative database. Respiratory virus surveillance data and statistics on ambient air temperature were obtained. Vaccine use in different birth cohorts was derived from the Quebec City Immunization Registry. Additive and multiplicative Poisson regression models were applied to estimate attributable fractions. Age, month of birth, ambient temperature, and respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and influenza-positive test proportions were significant predictors of LRTI hospitalizations. No substantial differences were observed in cohorts exposed to the 7-valent or 10-valent pneumococcal conjugate vaccines. In the additive model, the fraction of hospitalizations explained by temperature variation was 37%, whereas RSV circulation explained 28%, hMPV 4% and influenza 1%. Complex interplay between biological, environmental and social mechanisms may explain the important role of ambient air temperature in predicting LRTI hospitalization risk in young children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".