Influência do Período Pandêmico do COVID-19 em Internamentos Prevalentes Pediátricos em um Hospital Público
Bibliographic record
Abstract
The present study aims to analyze the profile of the most prevalent causes of hospitalizations in children under 10 years of age, in a public hospital in Pernambuco, during the COVID-19 pandemic period. This is a quantitative, cross-sectional, retrospective and descriptive research. DATASUS was used to collect data, using a data collection instrument built by the authors. The data were organized and processed, under simple statistics, using Microsoft Excel, which allowed the creation of tables and graphs to present the results and discuss the findings. Of the total number of hospitalizations, 56% were male, 77% were mixed race/color, the most common age range was <1 year for general causes and 1-4 years for external causes. As for the nature of the service, 99% was urgent; Among the causes, 60% were due to conditions in the neonatal period and 27% respiratory diseases. The pandemic changed the pattern of hospitalizations with an initial drop in hospitalizations for general causes and a subsequent increase in 2021, concentrated in respiratory causes, in addition to an increase in hospitalizations for external causes. It is concluded that the pandemic period affected the most prevalent causes of pediatric hospitalizations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".