Impact of COVID-19 pandemic on foreign body ingestion in children and adolescents: a cross-sectional study
Bibliographic record
Abstract
Abstract Introduction Foreign body ingestions (FBI) are a common reason for emergency department (ED) visits in children. We hypothesized that increased time spent at home by children due to COVID-19 restrictions could contribute to a rise in FBI ingestion rate and severity. Our primary objective was to evaluate the number of FBI cases at a Canadian tertiary paediatric hospital in Montreal during the pandemic as compared to the two previous years. Methods Children assessed at CHU Sainte-Justine ED for FBI between March 2018-February 2020 (pre-pandemic) and March 2020-February 2021 (pandemic) were included. FBI ratio was calculated by dividing the number of FBI cases by the total number of ED visits. Differences between the two groups were analyzed by Student’s t-test or Chi-square test. Results A total of 614 cases of FBI (median age, 3.5 years; 54% male) were included. The ratio of FBI doubled during the pandemic: 51.7 cases/10,000 ED visits vs 24.0 cases/10 000 visits in the pre-pandemic group (P = 0.0002). The overall number of cases increased significantly during the pandemic period from an average 15.5 cases per month to 20.2. Almost one-fourth of the cohort was hospitalized at similar rates during both observation periods. Conclusions The ratio of FBI cases increased significantly during the pandemic in comparison with the two previous years. The high hospitalization rates, although stable during the pandemic, underline the significant morbidity associated with paediatric FBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".