Risk factors for severe and fatal childhood unintentional injury: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Unintentional injuries are a leading cause of death among children aged 1-19 years worldwide. Systematic reviews assessing various risk factors for different childhood injuries have been published previously. However, most of the related literature does not distinguish minor from severe or fatal injuries. This study aims to describe and summarize the current knowledge on the determinants of severe and fatal childhood unintentional injuries and to discuss the differences between risk factors for all injuries (including minor injuries) and severe and fatal injuries. The study also aims to quantify the reduction in childhood injuries associated with a reduction in exposure to some of the identified risk factors in the Canadian population. METHODS: A systematic review and meta-analysis will be conducted by searching MEDLINE, Embase, CINAHL, and Web of Science. Observational and experimental cohort studies assessing children and adolescents aged ≤ 19 years old and determinants of severe and fatal unintentional injury, such as personal behaviors, family and environmental characteristics, and socioeconomic and geographic context, will be eligible. The main outcome will be a composite of any severe or fatal unintentional injuries (including burns, drowning, transport-related injuries, and falls). Any severity measurement scale will be accepted as long as severe cases require at least one hospital admission. Two authors will independently screen for inclusion, extract data, and assess the quality of the data using the Cochrane ROBINS-E tool. Meta-analysis will be performed using random effects models. Subgroup analyses will examine age subgroups and high- vs low-income countries. Sensitivity analysis will be conducted after restricting analyses to studies with a low risk of bias. Attributable fractions will be computed to assess the burden of identified risk factors in the Canadian population. DISCUSSION: Given the numerous determinants of childhood injuries and the challenges that may be involved in identifying which individuals should be prioritized for injury prevention efforts, this evidence may help to inform the identification of high-risk children and prevention interventions, considering the disproportionate consequences of severe and fatal injuries. This evidence may also help pediatric healthcare providers prioritize counseling messaging. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023493322.
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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 | 0.052 | 0.053 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.009 |
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".