Survival period in fatal road traffic accidents
Bibliographic record
Abstract
The intervals between injury and death in 450 fatalities after road accidents were analyzed and correlated with other data. Pedestrians were commonest group involved (28.7%) followed by occupants of cars (25.8%) and Motorcyclists (23.1%). In round figures, a third of series died with in half an hour, two third by 12 hours and three Quarter by 48 hours after accidents. Ninety percent of victims died with in one week. The pattern of survival time could be divided into three phases.There was an early steep decline the 38.4% dying during first half an hour and 40% with in one hour of accident. This was followed by longer stage during which rate of dying decreased progressively in logarithmic fashion. The cumulative tolls were 60.2% by 6 hours, 67.8% by 12 hours, 77.1%by 24 hours and 90% by 7 days. Between 7 to 14 days another 7% died.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".