Injuries Experienced by Infant Children: A Population-Based Epidemiological Analysis
Bibliographic record
Abstract
OBJECTIVE: Injuries to infant children are an important health concern, yet there are few population-based analyses from which to develop prevention initiatives. This study describes the external causes, natures, and disposition from an emergency department of infants with injuries for a geographically distinct population in Eastern Ontario. METHODS: Epidemiologic analysis of emergency-based surveillance data (1994-2000) for infants (<12 months old) from the Kingston sites of the Canadian Hospitals Injury Reporting and Prevention Program. RESULTS: A total of 990 cases of injury to infants were identified, of which 217 (21.9%) required significant medical intervention. Leading causes of injury were falls (605/990; 61.1%), ingestion injuries (65/990; 6.6%), and burns (56/990; 5.7%). Common types of falls experienced were: from furniture (229/605; 37.9%), being dropped (92/605; 15.2%), in car seats (73/605; 12.1%), down stairs (63/605; 10.4%), or in a child walker (42/605; 6.9%). The observed patterns of injury changed according to the ages of the children. Vignettes are used to illustrate recurrent injury patterns (falls, physical vulnerability, burns and ingestions, equipment injuries). CONCLUSION: The results indicate the relative importance of several external causes of injury and how these vary by age group. This population-based information is also useful in establishing rational priorities for prevention, and the targeting of interventions toward responsible authorities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".