A Retrospective Analysis of Mortality From 2015 Gorkha Earthquakes of Nepal: Evidence and Future Recommendations
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the mortality pattern due to Gorkha earthquakes in 2015 and review the response and recovery efforts immediately following the earthquakes. METHODS: Data from published reports of the Nepal Police showed over 8000 deaths. These death counts were categorized by gender, ethnicity, and age groups (interval of 5 years). The mortality rate was calculated (per 100 000 population), using the projected population as the denominator as of April 2015. RESULTS: Children < 10 years and older adults > 55 years showed a higher rate of deaths, with similar trends for the most affected districts. Almost 8 more females' deaths were reported per 100 000 population compared with their male counterparts. There was a higher death rate from Province 3 with a notable gender difference: Nearly 20 more females' deaths were reported per 100 000 population compared with their male counterparts. There was a higher death rate in mountains (542.4 per 100 000) compared with hills (55.0 per 100 000) and the southern Terai region (0.96 per 100 000) of Nepal. CONCLUSIONS: Young and older adults, female, and residents of remote, mountainous regions of Nepal were vulnerable to the earthquakes. Future earthquake preparedness should focus on the vulnerable population by age and gender and the geographical accessibility.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".