THE DISTRIBUTION AND CHARACTERISTICS OF LIGHTNING INJURIES AMONG RESIDENTS IN A RURAL AREA IN SRI LANKA.
Bibliographic record
Abstract
Background: Lightning occurs most commonly in the tropical countries, yet due to under-reporting of data, both developed and developing countries didn?t aware the actual problem. Thus, proper reporting and management of the victims related to lighting are crucial. Aim and Objective: To describe the distribution and characteristics of the lightning-related lifetime injuries among residents in a rural area in Sri Lanka Methods: Weconducteda cross-sectional survey among 510 residents in the Medical Officer of Health area, Kiriella. Among them, we selected residents who reported the lightning-related lifetime injuries and interviewed them using an interviewer-administered questionnaire. Results: The lightning-related lifetime injuries were 18 (3.6%). Among the victims, 12 (66.7%) were males, and 6 (33.3%) were females. Most (n=13, 72.2%) were 18 to 45 years of age, when injured to lightning. Eight (44.4%) lightning strikes happened during the period from 2004 to 2013, and most of the lightning-related injuries were reported from noon to evening (n=10, 55.6%). The most (n=9, 50.0%) affected body part was the head. Males were 1.34 times increased risk for lightning compared to females (Relative risk 1.34, 95% confidence interval 0.51-3.50). Further, residents of age 18 to 45 years were highly vulnerable to lightning (Relative risk 3.07, 95% confidence interval 1.11-8.49). Conclusions:Lightning-related injuries are more common among younger males in the selected rural area in Sri Lanka. Therefore it is recommended to conduct awareness programmes on preventive measures among at-risk population and to introduce protective measures at residences to mitigate the loss and damage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".