Deaths and Injuries from House Fires
Bibliographic record
Abstract
BACKGROUND: We sought to define the factors associated with house fires and related injuries by analyzing the data from population-based surveillance. METHODS: For 1991 through 1997, we linked the following data for Dallas: records from the fire department of all house fires (excluding fires in apartments and mobile homes), records of patients transported by ambulance, hospital admissions, and reports from the medical examiner of fatal injuries. RESULTS: There were 223 injuries (91 fatal and 132 nonfatal) from 7190 house fires, for a rate of 5.2 injured persons per 100,000 population per year. Rates of injury related to house fires were highest among blacks (relative risk, 2.8; 95 percent confidence interval, 2.1 to 3.6) and in people 65 years of age or older (relative risk, 2.6; 95 percent confidence interval, 1.9 to 3.5). Census tracts with low median incomes had the highest rates of injury related to house fires (relative risk as compared with census tracts with high median incomes, 8.1; 95 percent confidence interval, 2.5 to 32.0). The rate of injuries was higher for fires that began in bedrooms or living areas (relative risk, 3.7); that were started by heating equipment, smoking, or children playing with fire (relative risk, 2.6); or that occurred in houses built before 1980 (relative risk, 6.6). Injuries occurred more often in houses without functioning smoke detectors (relative risk, 1.5; 95 percent confidence interval, 1.0 to 2.4). The prevalence of functioning smoke detectors was lowest in houses in the census tracts with the lowest median incomes (P<0.001). CONCLUSIONS: Rates of injuries related to house fires are highest in elderly, minority, and low-income populations and in houses without functioning smoke detectors. Efforts to prevent injuries and deaths from house fires should target these populations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".