RETROSPECTIVE STUDY ON RAILWAY-RELATED DEATHS IN SOUTH INDIA.
Bibliographic record
Abstract
Background: Railways form an important mode of transportation worldwide. People in India use the railway as an important means of transport as it is economical and easily available both for local service and for distant modes of transportation. Along with the advantages that railways are providing as a mode of transport, railway deaths are one of the important fatal outcomes in this part of the world. Aim of the work: To study railway-related deaths concerning age, sex, pattern of injuries, and causes of death. Methods: In the current study done in Bangalore, India, the autopsy and police records were scrutinized to collect information to obtain an overview of deaths occurring due to railway injuries and deaths occurring in trains. The study analyses several demographic factorial details and common regions of the body that are involved in railway-related deaths. Results: Apart from the common injuries encountered in railway-related deaths causing death, this study also gives information about type V of railway-related deaths (Unusual incidents). Conclusion: The railway-related deaths encountered include both natural and unnatural causes. Recommendation: Strict rules and regulations should be initiated along with safety measures from lawmakers to avoid accidents as well as suicides due to railway deaths, as the injuries sustained will be fatal in most cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".