Fatal and non-fatal fire injuries in England 1995-2004: time trends and inequalities by age, sex and area deprivation
Bibliographic record
Abstract
AIM: To examine time trends and deprivation gradients in fire-related deaths and injuries. METHODS: A cross-sectional study and time trend analysis using data on fire casualties in England between 1995 and 2004 obtained from the Department for Communities and Local Government. Injury rates were calculated assuming a Poisson distribution. Incidence rate ratios (IRRs) were calculated to compare changes in deprivation gradients over time. RESULTS: There were significant reductions in fatal and non-fatal fire injuries in children (fatal injuries IRR chi(2)(1) = 11.18, P < 0.001; non-fatal injuries IRR chi(2)(2) = 61.44, P < 0.001), adults (fatal injuries IRR chi(2)(1) = 15.99, P < 0.001; non-fatal injuries IRR chi(2)(2) = 183.25, P < 0.001) and older people (fatal injuries IRR chi(2)(1) = 56.88, P < 0.001; non-fatal injuries IRR chi(2)(2) = 54.09, P < 0.001) between 1995 and 2004. Adult and child fire deaths were most commonly caused by smokers' materials (e.g. cigarettes, cigars and tobacco), and cigarette lighters and matches, respectively. Cooking appliances caused most non-fatal fire injuries. Injury rates increased with increasing levels of deprivation and deprivation gradients did not change over 10 years. CONCLUSIONS: Fire prevention interventions should promote the safe use of cooking and heating appliances and the responsible use of smokers' materials, lighters and matches, and should target those at greater risk of fire, including the socially disadvantaged.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".